Academic literature on the topic 'Field transfer trailer'
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Journal articles on the topic "Field transfer trailer"
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Shaffer, Robert M., and James M. Keesee. "Keeping Mud Off the Highway During Wet-Weather Logging." Southern Journal of Applied Forestry 15, no. 1 (February 1, 1991): 50–53. http://dx.doi.org/10.1093/sjaf/15.1.50.
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Abstract Mud transfer from soil-based logging roads to paved public highways by log trucks operating during muddy conditions is a major problem for southern logging contractors. Mud transfer can result in fines, traffic accidents, lawsuits, and loss of production. Four devices that could be attached to a log trailer to remove mud from the trailer's dual tires were designed, constructed, and field tested. A simple and inexpensive "bar and scraper" was found to be particularly effective, removing 85% of the mud accumulated during the control phase of the study. South J. Appl. For. 15(1):50-53.
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Sarwary, A. M. E., D. F. Stegeman, L. P. J. Selen, and W. P. Medendorp. "Generalization and transfer of contextual cues in motor learning." Journal of Neurophysiology 114, no. 3 (September 2015): 1565–76. http://dx.doi.org/10.1152/jn.00217.2015.
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We continuously adapt our movements in daily life, forming new internal models whenever necessary and updating existing ones. Recent work has suggested that this flexibility is enabled via sensorimotor cues, serving to access the correct internal model whenever necessary and keeping new models apart from previous ones. While research to date has mainly focused on identifying the nature of such cue representations, here we investigated whether and how these cue representations generalize, interfere, and transfer within and across effector systems. Subjects were trained to make two-stage reaching movements: a premovement that served as a cue, followed by a targeted movement that was perturbed by one of two opposite curl force fields. The direction of the premovement was uniquely coupled to the direction of the ensuing force field, enabling simultaneous learning of the two respective internal models. After training, generalization of the two premovement cues' representations was tested at untrained premovement directions, within both the trained and untrained hand. We show that the individual premovement representations generalize in a Gaussian-like pattern around the trained premovement direction. When the force fields are of unequal strengths, the cue-dependent generalization skews toward the strongest field. Furthermore, generalization patterns transfer to the nontrained hand, in an extrinsic reference frame. We conclude that contextual cues do not serve as discrete switches between multiple internal models. Instead, their generalization suggests a weighted contribution of the associated internal models based on the angular separation from the trained cues to the net motor output.
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Sheppard, Daniel J., Sherrie A. Jones, Daniel P. Westra, and Joyce J. Madden. "Simulator Evaluation of Instructional and Design Features for Training Helicopter Shipboard Landing." Proceedings of the Human Factors Society Annual Meeting 32, no. 18 (October 1988): 1261–65. http://dx.doi.org/10.1177/154193128803201815.
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The effects of four instructional issues and one simulator design feature for training helicopter shipboard landing on small ships were tested in the Vertical Take-off and Landing Simulator (VTOL) at the Visual Technology Research Simulator (VTRS), Naval Training Systems Center. They were: (1) field of view (VTRS versus a test field of view), (2) task chaining (segmented backward chaining versus whole task training), (3) augmented cueing (augmented cueing versus no augmented cueing), (4) length of training (18, 27, and 36 trials), and (5) the timing of seastate introduction (early versus late). The experiment utilized an in-simulator transfer-of-training paradigm in which pilots who were not proficient in the helicopter shipboard landing task were trained under one of several experimental conditions, then tested on the transfer condition (that represented maximum realism) in the simulator. Thirty-two pilots each completed a total of 54 trials (36 training, 18 transfer). Pilots were tested in the transfer condition (six trials) after their 18th, 27th, and 36th training trial. Of the experimental instructional issues, task chaining had the largest effect, with better performance in all segments of the task for pilots who were trained with the backward-chaining sequence, than for pilots who received whole task training. Augmented cueing did not yield the transfer performance anticipated. Seastate introduction had no effect on performance. Field of view had some marginal effects on vertical performance in the hover, with better performance for pilots who were trained with the combination VTRS field-of-view and backward-chaining. Results suggest a diminished rate of learning after 33 simulator trials (includes 27 training trials and six transfer trials of the first probe).
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Nguyen, Minh Ngoc, Nha Thanh Nguyen, and Thien Tich Truong. "Estimation of heat transfer parameters by using trained POD-RBF and Grey Wolf Optimizer." Vietnam Journal of Mechanics 42, no. 4 (December 27, 2020): 401–14. http://dx.doi.org/10.15625/0866-7136/15015.
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The article presents a numerical model for estimation of heat transfer parameters, e.g. thermal conductivity and convective coefficient, in two-dimensional solid bodies under steady-state conduction. This inverse problem is stated as an optimization problem, in which input is reference temperature data and the output is the design variables, i.e. the thermal properties to be identified. The search for optimum design variables is conducted by using a recent heuristic method, namely Grey Wolf Optimizer. During the heuristic search, direct heat conduction problem has to be solved several times. The set of heat transfer parameters that lead to smallest error rate between computed temperature field and reference one is the optimum output of the inverse problem. In order to accelerate the process, the model order reduction technique Proper-Orthogonal-Decomposition (POD) is used. The idea is to express the direct solution (temperature field) as a linear combination of orthogonal basis vectors. Practically, a majority of the basis vectors can be truncated, without losing much accuracy. The amplitude of this reduced-order approximation is then further interpolated by Radial Basis Functions (RBF). The whole scheme, named as trained POD-RBF, is then used as a surrogate model to retrieve the heat transfer parameters.
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Subedi, Bhawani Shankar. "Assessing the Effectiveness of Teacher Training Programs to Improve the Quality of School Education in Nepal." Journal of Training and Development 1 (July 31, 2015): 9–14. http://dx.doi.org/10.3126/jtd.v1i0.13084.
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Inadequate transfer of knowledge, skills, attitudes and behaviours from the training environment to the workplace environment has emerged as a global issue. Teachers’ training has not been an exception. Available literature on teacher training indicates that the contribution of training can be assessed at least on six dimensions- quality, access, equity, efficiency, teacher development and overall school development. Studies conducted in the area of teacher training or teacher professional development in the context of Nepal are also evident of lack of sufficient transfer of knowledge and skills from training to workplace. There are several factors facilitating or inhibiting the extent of such transfer. Research shows that the training of teachers has contributed and can positively influence quality of education if stakeholders are made aware of and well informed about the quality and relevance of training and development interventions carefully designed and implemented for the capacity development of teachers, teacher educators or trainers. This article has been derived from the synopsis of a comprehensive study conducted in Nepal and concluded in March 2010. Data bases of 4033 trained teachers of 45 schools from 25 sample districts were studied. This study was a blending of quantitative as well as qualitative approaches. Nine education experts and 22 field researchers were involved. The author was the team leader of the study. The only academic purpose of this article is to inspire excellence in teaching, learning and performance by means of professionalism and capacity building of teachers, teacher trainers and their employers. DOI: http://dx.doi.org/10.3126/jtd.v1i0.13084 Journal of Training and Development Vol.1 2015: 9-14
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Houldin, Adina, Romeo Chua, Mark G. Carpenter, and Tania Lam. "Limited interlimb transfer of locomotor adaptations to a velocity-dependent force field during unipedal walking." Journal of Neurophysiology 108, no. 3 (August 1, 2012): 943–52. http://dx.doi.org/10.1152/jn.00670.2011.
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Several studies have demonstrated that motor adaptations to a novel task environment can be transferred between limbs. Such interlimb transfer of motor commands is consistent with the notion of centrally driven strategies that can be generalized across different frames of reference. So far, studies of interlimb transfer of locomotor adaptations have yielded disparate results. Here we sought to determine whether locomotor adaptations in one (trained) leg show transfer to the other (test) leg during a unipedal walking task. We hypothesized that adaptation in the test leg to a velocity-dependent force field previously experienced by the trained leg will be faster, as revealed by faster recovery of kinematic errors and earlier onset of aftereffects. Twenty able-bodied adults walked unipedally in the Lokomat robotic gait orthosis, which applied velocity-dependent resistance to the legs. The amount of resistance was scaled to 10% of each individual's maximum voluntary contraction of the hip flexors. Electromyography and kinematics of the lower limb were recorded. All subjects were right-leg dominant and were tested for transfer of motor adaptations from the right leg to the left leg. Catch trials, consisting of unexpected removal of resistance, were presented after the first step with resistance and after a period of adaptation to test for aftereffects. We found no significant differences in the sizes of the aftereffects between the two legs, except for peak hip flexion during swing, or in the rate at which peak hip flexion adapted during steps against resistance between the two legs. Our results indicate that interlimb transfer of these types of locomotor adaptation is not a robust phenomenon. These findings add to our current understanding of motor adaptations and provide further evidence that generalization of adaptations may be dependent on the movement task.
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Myers, Carole. "Core skills and transfer in the youth training schemes: A field study of trainee motor mechanics." Journal of Organizational Behavior 13, no. 6 (November 1992): 625–32. http://dx.doi.org/10.1002/job.4030130608.
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Arnold, Gabriel, and Malika Auvray. "Perceptual Learning: Tactile Letter Recognition Transfers Across Body Surfaces." Multisensory Research 27, no. 1 (2014): 71–90. http://dx.doi.org/10.1163/22134808-00002443.
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Visual-to-tactile sensory substitution devices are designed to assist visually impaired people by converting visual stimuli into tactile stimuli. The important claim has been made that, after training with these devices, the tactile stimuli can be moved from one body surface to another without any decrease in performance. This claim, although recurrent, has never been empirically investigated. Moreover, studies in the field of tactile perceptual learning suggest that performance improvement transfers only to body surfaces that are closely represented in the somatosensory cortex, i.e. adjacent or homologous contralateral body surfaces. These studies have however mainly used discrimination tasks of stimuli varying along only one feature (e.g., orientation of gratings) whereas, in sensory substitution, tactile information consists of more complex stimuli. The present study investigated the extent to which there is a transfer of tactile letter learning. Participants first underwent a baseline session in which the letters were presented on their belly, thigh, and shin. They were subsequently trained on only one of these body surfaces, and then re-tested on all of them, as a post-training session. The results revealed that performance improvement was the same for both the trained and the untrained surfaces. Moreover, this transfer of perceptual learning was equivalent for adjacent and non-adjacent body surfaces, suggesting that tactile learning transfer occurs independently of the distance on the body. A control study consisting of the same baseline and post-training sessions, without training in between, revealed weaker improvement between the two sessions. The obtained results support the claim that training with sensory substitution devices results in a relative independence from the stimulated body surface.
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Zheng, Zhong, Jinfei Wang, Bo Shan, Yongjun He, Chunhua Liao, Yanghua Gao, and Shiqi Yang. "A New Model for Transfer Learning-Based Mapping of Burn Severity." Remote Sensing 12, no. 4 (February 21, 2020): 708. http://dx.doi.org/10.3390/rs12040708.
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In recent years, global forest fires have occurred more frequently, seriously destroying the structural functions of forest ecosystem. Mapping the burn severity after forest fires is of great significance for quantifying fire’s effects on landscapes and establishing restoration measures. Generally, intensive field surveys across burned areas are required for the effective application of traditional methods. Unfortunately, this requirement could not be satisfied in most cases, since the field work demands a lot of personnel and funding. For mapping severity levels across burned areas without field survey data, a semi-supervised transfer component analysis-based support vector regression model (SSTCA-SVR) was proposed in this study to transfer knowledge trained from other burned areas with field survey data. Its performance was further evaluated in various eco-type regions of southwestern United States. Results show that SSTCA-SVR which was trained on source domain areas could effectively be transferred to a target domain area. Meanwhile, the SSTCA-SVR could maintain as much spectral information as possible to map burn severity. Its mapped results are more accurate (RMSE values were between 0.4833 and 0.6659) and finer, compared to those mapped by ∆NDVI-, ∆LST-, ∆NBR- (RMSE values ranged from 0.7362 to 1.1187) and SVR-based models (RMSE values varied from 1.7658 to 2.0055). This study has introduced a potentially efficient mechanism to map burn severity, which will speed up the response of post-fire management.
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Gupta, Keshav, Rajneesh Rani, and Nimratveer Kaur Bahia. "Plant-Seedling Classification Using Transfer Learning-Based Deep Convolutional Neural Networks." International Journal of Agricultural and Environmental Information Systems 11, no. 4 (October 2020): 25–40. http://dx.doi.org/10.4018/ijaeis.2020100102.
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The ever-growing population of this world needs more food production every year. The loss caused in crops due to weeds is a major issue for the upcoming years. This issue has attracted the attention of many researchers working in the field of agriculture. There have been many attempts to solve the problem by using image classification techniques. These techniques are attracting researchers because they can prevent the use of herbicides in the fields for controlling weed invasion, reducing the amount of time required for weed control methods. This article presents use of images and deep learning-based approach for classifying weeds and crops into their respective classes. In this paper, five pre-trained convolution neural networks (CNN), namely ResNet50, VGG16, VGG19, Xception, and MobileNetV2, have been used to classify weed and crop into their respective classes. The experiments have been done on V2 plant seedling classification dataset. Amongst these five models, ResNet50 gave the best results with 95.23% testing accuracy.
Dissertations / Theses on the topic "Field transfer trailer"
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Pokorný, Emil. "Návrh konstrukce překládacího vozu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442813.
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The aim of the diploma thesis is the structural design of the field transfer trailer designed for harvesting work in agriculture. The first part of the thesis contains an overview of similar construction solutions and a summary of legislative requirements related to the device. In the next part is performed a description of the individual structural units, the design of the drive and the design of the screw conveyors. Based on the stated load conditions, the strength analysis of the large – volume hopper is processed, which is evaluated in the final part. An integral part of the thesis is also a partial drawing documentation. This diploma thesis was created in cooperation with the company ZDT Nové Veselí.
Book chapters on the topic "Field transfer trailer"
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Reynolds, Rodney. "The Transfer of Learning." In Advances in Human Resources Management and Organizational Development, 81–89. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7665-6.ch006.
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The focus of this chapter is on insights for and evaluation of if the transfer of learning by training and development professionals will sustain the training field. The chapter begins with the need for a model of learning. Then there is a section on a proposed evaluation of the trainer presentation of a model of learning or training development. The chapter then progresses to a proposed measure of the potential for the transfer of learning and the need to assess self-efficacy with gradations of attainments on the training topic. Finally, the chapter turns to trainee engagement and to having a results orientation for a training program.
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Vila, Ignacio, Iván Couto-González, and Beatriz Brea-García. "Basic Principles in Microvascular Anastomosis and Free Tissue Transfer." In Vascular Biology - Selection of Mechanisms and Clinical Applications. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.91917.
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Free tissue transfer pursues the best functional and aesthetic results in reconstructive surgery. As these techniques completely maximise the donor tissues’ disposability, these treatments have become a first-line option in many situations. When the donor site is taken form the same patient, these surgeries are often referred to as autotransplants. Free tissue transfer sustains in microvascular anastomosis, which are defined by a vessel lumen diameter inferior to 3 mm. Particular attention to some details is important in these techniques, as, for example, to preclude any damage to the vessel walls or any leakage in the microvascular anastomosis. But the success of these techniques does not only depend on an adequate vascular suture, but also on a constellation of details that must be taken into account. These go from the availability of a trained team, to the ergonomics of the surgeon, through the scrupulous cleanliness of the surgical field.
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Cheah, Agnes Ruey Chyi, Kim Ho Yeap, Kee Choon Yeong, and Kazuhiro Hirasawa. "Biologically-Inspired Wireless Power Transmission System." In Advances in Environmental Engineering and Green Technologies, 27–50. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9792-8.ch002.
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This chapter gives an overview on Wireless Power Transfer (WPT) systems and the existing biologically-inspired architectures which could be used to optimize the efficiency of the systems. In general, wireless power transmission could be categorized into near-field and far-field mechanisms. At present, one of the most immediate issues to be solved is the relatively low efficiency in a WPT system. The system's efficiency decreases gradually as the distance increases. To ensure highest efficiency, the path with the shortest distance and the lowest interference is to be selected. In this case, a few of the most popular biologically-inspired algorithms, i.e. Firefly Algorithm(FA), Simulated Annealing (SA), the Ant Colony Optimization (ACO) and Genetic Algorithms (GAs) could be employed for efficiently solving different optimization problems. In short, these algorithms mentioned involve the emulation of fireflies flash signals for mating, exploitation of the metallurgy process, nature of ants leaving pheromones along the trails traveled and also the mimic of gradual evolution of organisms.
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Walker, Melissa. "A Place Where Women Can Feel Valued, or Why Academic Professional Associations Matter, Especially for Women." In Sisterly Networks, 56–75. University Press of Florida, 2020. http://dx.doi.org/10.5744/florida/9780813066615.003.0004.
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The Southern Association for Women Historians provided a place where female historians felt validated and emboldened. By providing this space over the past five decades, the SAWH has done two important things: advance the careers of individual female historians while encouraging, developing, and legitimizing the study of women’s history. In the process, as several of the scholars here have already suggested, the SAWH helped transform the historiography of the American South by refocusing many of the lenses that scholars have trained on the past. The history of the SAWH demonstrates the crucial role that scholarly professional associations play in shaping fields of knowledge and the careers of individual scholars.
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Hicks, Kenny, and Randall W. Engle. "Cognitive Perspectives of Working Memory Training." In Cognitive and Working Memory Training, 3–13. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199974467.003.0001.
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Working memory training is an emergent field aimed at improving general cognitive abilities through targeted brain exercises. The prospect of improving cognitive abilities, such as attention control, comprehension, and reasoning, has piqued the interest of the scientific community and the general public alike. If cognitive abilities like working memory capacity can be improved, it is assumed that this improvement will result in benefits to a broad range of real-world abilities associated with working memory capacity, including reading comprehension, math performance, and attention control. Thus, the goal of working memory training is to demonstrate broad transfer to tasks that involve the same components of working memory that were targeted during training. Therefore, improvements should be observed on a broad range of tasks that tap the ability being trained. This is measured by observing the difference between pre- and posttest performance on cognitive tasks that subjects have not practiced. The aim of this chapter is to summarize the extant literature on working memory training and then to pose a series of questions to researchers investigating the efficacy of working memory training.
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Huhtinen, Aki-Mauri, Arto Hirvelä, and Tommi Kangasmaa. "The Opportunities of National Cyber Strategy and Social Media in the Rhizome Networks." In Cyber Law, Privacy, and Security, 923–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8897-9.ch043.
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Securing the society is a central task of the state. In the present day, as well as in the future, knowledge and information are evermore closely tied to electronic data transfer. Finland's newly published Cyber Security Strategy depicts how the government safeguards electronic data transfer, that is, information security against different threat and risk scenarios. Cyberspace has a human element and a technological element. It is a way to influence and affect society. It may be used to influence minds or to attack the physical world, for example by disrupting traffic control. But cyberspace cannot exist without people. The cyberspace offers the platform so called strategic communication. Strategic communication is a concept that unites the efforts of governmental organisations to influence people in support of national interests. Formal organisations and institutions are often seen as being opposed or resistant to change. Social Media and the cyber domain can offer many opportunities but also unknown threats and risks. In this paper we argue that securing an organisation is a living and continuously changing process. Deleuze and Guattari (1983) present the concept of a rhizome, meaning a dynamic weed formation which, opposed to the arboreal and hierarchical structure of the tree, involves spontaneous, unpredictable and distant connections between heterogeneous elements (Linstead & Thanem 2007, 1484) Strategic Communication is the focus of a heated discussion in the military field: How can militaries be credible and uphold the high standards of democracy within the asymmetric and complex battlefield? To be credible one must act according to what is said. The challenge is that the scene is global in the information age. The act, the actor, the scene, the purpose – all are exposed to a global audience through cyberspace on a very short notice. The most effective way of showing the scene and actors is an audiovisual product. Examples of this come from all conflict zones. Militaries are establishing Youtube channels and supplying material from intense fighting and frontline action. The solution for supporting the organisation's own arguments has been the Combat Camera capability, in other words media-trained soldiers who are where the main effort is happening. Within the spirit of strategic communication, the cyber strategy itself is one way to operationalise cyber security by announcing measures to be taken against cyber attacks. In this article the authors reflect on the Finnish Cyber Strategy and Strategic Communications from a phenomenological perspective.
Conference papers on the topic "Field transfer trailer"
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M. Milani, Pedro, Julia Ling, and John K. Eaton. "Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90498.
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Abstract The design of film cooling systems relies heavily on Reynolds-Averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbulent heat flux models, which are based on isotropic diffusion with a fixed turbulent Prandtl number (Prt), fail to accurately predict heat transfer in film cooling flows. In the present work, machine learning models are trained to predict a non-uniform Prt field, using various datasets as training sets. The ability of these models to generalize beyond the flows on which they were trained is explored. Furthermore, visualization techniques are employed to compare distinct datasets and to help explain the cross-validation results.
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Mu¨ller, N., J. Einzinger, T. Lepach, S. Kra¨mer, S. Thum, and R. Schilling. "Application of a Multi Level CFD-Technique for the Design Optimisation of Hydraulic Machinery Bladings." In ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56310.
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To achieve maximum hydraulic efficiency at a wide range of operation, efficient numerical tools are inevitable for the shape optimisation of hydraulic machinery, e.g. rotodynamic pumps and water turbines. A suitable geometry representation by means of B-Spline techniques for the three-dimensionally (3D) curved bladings is necessary as well as a powerful CFD technique in order to evaluate and analyze the hydraulic properties of the design. Both of these modules are integrated in one program system so as to allow the design engineer to achieve maximum performance. There is a natural contradiction between the numerical effort to describe a complex geometry and to compute the flow field as exactly as necessary for an accurate evaluation, and the industrial need to lower the costs and the duration of development of a new design. The 3D curved bladings of rotodynamic pumps and water turbines are characterized by a big number of parameters. Thus, the shape optimisation of this type of blades is still a problem to be solved by an efficient approach. To simplify this complexity, the blade profiles are defined by a reduced set of B-Spline describers on a number of stream surfaces in the conformal mapping representation. This provides an efficient and quick method for modifying the blade shape. To reduce the numerical effort in an iterative optimisation process, a new idea of using the Multi Level CFD-Technique (MLCFD) is described more detailed. The CFD tools used in this process, can be chosen by different criterions such as accuracy on the one hand and computation time on the other hand: There are quasi-3D and full-3D Euler codes as well as quasi-3D and full-3D Navier-Stokes codes for the flow computation and analysis. Starting from an initial design which may come from an artificial neural network having been trained from an existing database, a rough estimation of the quality of a geometry modification can be performed by a quick quasi-3D Euler computation, whereas the final evaluation of the optimum design should be carried out by an accurate 3D Navier-Stokes code. The MLCFD-technique is applied to optimize the blade shape of a centrifugal pump impeller with a specific speed nq = 39 l/min. The numerical results show that significant improvements of the local and integral flow quantities as well as a remarkable reduction of the numerical effort may be reached by using this new approach.
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Nemeth, David, and Troy I. Walda. "The Pipe Inspection Lifecycle." In 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33594.
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Energy Transfer has implemented a new, comprehensive field-inspection system for the pipe inspection lifecycle that encompasses aerial observations, pipe exposures, foreign line crossings, in-line inspections, anomaly remediation, pipe inspection, and integrity sheet generation. In order to ensure the integrity of the pipe inspection program, the field inspection solution required full audit-trail capabilities, front-side data validation, and full integration with the corporate-wide GIS and Engineering Data Management System. Additionally, to ensure the success of the new inspection program, the inspection solution required a highly intuitive and field user-friendly interface, the ability to work equally well in both connected and disconnected environments, interactive mapping functionality, very high reliability, and a process-driven architecture. Energy Transfer owns and operates approximately 43,000 miles of natural gas, natural gas liquids, refined products, and crude oil pipelines. Due to the size and diversity of Energy Transfer’s assets, the corporate GIS system must be distributed across seven independent instances consisting of server pools and large-scale relational database management systems (RDBMS). Although each system must be functionally independent, the field inspection system and the inspection process must interact with each server and RDBMS instance with equal functionality and be able to report on all pipe inspection activities across the enterprise. The inspection system is used by over 1,200 Energy Transfer employees and contractors, and approximately 15,000 inspections are performed annually. The system supports a variety of devices, such as: laptops, tablet computers, iOS devices (i.e., iPads, iPhones), and Android devices. Whether on foot, in vehicles or aircraft, users can enter information from the platform that best meets the needs of their individual environment. Information collected on any device is available for continuance of the pipe inspection lifecycle on any other device and is available in real time at the corporate offices via a Web portal. The Web portal provides visualization tools for both business and engineering analysis such as progress tracking and remediation planning. These functions are supported through the portal’s integrated mapping, dash boarding, and a reporting functionality that includes advanced search capabilities for both comparative and predictive analysis. In addition to utilization for the pipe inspection lifecycle, the inspection system is being used for a variety of other inspection and regulatory compliance-related activities, including: cathodic protection, incident reporting, corrosion assessment, DOT structure location, MAOP-MOP establishment, shallow cover, unmetered gas loss, and many more right-of-way related activities.
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Fuchi, Kazuko W., Eric M. Wolf, David S. Makhija, Nathan A. Wukie, Christopher R. Schrock, and Philip S. Beran. "Investigation of Analysis and Gradient-Based Design Optimization Using Neural Networks." In ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/smasis2020-2241.
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Abstract Design optimization of adaptive systems requires a robust analysis method that can accommodate various changes in design and boundary conditions. In this work, physics-informed neural networks (PINNs) are used to approximate solutions to differential equations across a range of problem parameter values. This mesh-free method simply requires residual evaluation at sampling points within the analysis domain and along boundaries, and the training process does not require any reference problem to be solved through conventional solution methods. The trained model can be used to predict the solution field, conduct parameter space analysis and design optimization. Using automatic differentiation, the design objective and their derivatives can be computed as a post process for a gradient-based design optimization. The method is demonstrated in a 1D heat transfer problem governed by the steady-state heat equation. Use of the PINN model for design optimization is illustrated in a problem of finding a material transition location to minimize temperature at a specified location. The PINN model that does not include problem parameters as input can be trained to within 0.05% error. PINN models that involve problem parameters as inputs are more difficult to train, especially when the input-to-output relationship is complex.
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He, Dongxiao, Wenze Song, Di Jin, Zhiyong Feng, and Yuxiao Huang. "An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/794.
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Markov Random Field (MRF) has been successfully used in community detection recently. However, existing MRF methods only utilize the network topology while ignore the semantic attributes. A straightforward way to combine the two types of information is that, one can first use a topic clustering model (e.g. LDA) to derive group membership of nodes by using the semantic attributes, then take this result as a prior to define the MRF model. In this way, however, the parameters of the two models cannot be adjusted by each other, preventing it from really realizing the complementation of the advantages of the two. This paper integrates LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly. However, LDA is a directed graphic model whereas MRF is undirected, making their integration a challenge. To handle this problem, we first transform LDA and MRF into a unified factor graph framework, allowing sharing the parameters of the two models. We then derive an efficient belief propagation algorithm to train their parameters simultaneously, enabling our approach to take advantage of the strength of both LDA and MRF. Empirical results show that our approach compares favorably with the state-of-the-art methods.
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Viano, Andrea, Gabriele Ottino, Luca Ratto, and Giuseppe Spataro. "Coupled CFD-ANN Procedure for Extending Heat Transfer Correlations Out of Their Range of Validity." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69707.
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The heat transfer coefficient and pressure losses are among the main parameters to be evaluated in gas turbine cooling network design. Due to the complexity of these estimates, correlation-based computations are typically used as a result of time-consuming and expensive experimental activities. One of the main problems that the industry has to face is that these correlations, based on non-dimensional experimental data, produce reliable results in a range of validity typically different from that encountered in gas turbine applications. This paper will present preliminary results of an innovative procedure based on CFD analyses and Artificial Neural Networks, able to extend correlation predictions out of their range of validity, without any additional experimental data. Well-known test cases were replicated by building corresponding CAD geometries which were discretized by means of appropriate meshes, resulting from grid-independence studies. CFD analyses, based on the RANS approach, were performed to overlay the computations of the Nusselt number obtained from experimental activities. A preliminary comparison among turbulence models was carried out to find one leading to a good agreement with the experimental data. Then, an optimization method, based on Evolutionary Algorithms, was applied to the CFD analyses in order to find the best set of constant values for the chosen turbulence model, leading to the most accurate prediction of the experimental dataset. The resulting ad hoc CFD model was adopted in order to analyse test case configurations characterized by parameters within and external to the correlation validity field, building a sufficiently wide feeding database. A feed-forward multi-layer neural network was selected among network architectures typically used in engineering applications for prediction analyses. ANNs were chosen because they enable the solution of these complex nonlinear problems by using simple computational operations. The selected Artificial Neural Network was trained by a back-propagation procedure on the CFD results regarding Nusselt number. The validation of the resulting ANN was performed comparing its outputs with experimental data external to the correlation range of validity, which had not been used in the training session. Good agreement has been found. Results are presented and discussed.
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Dou, Qi, Cheng Ouyang, Cheng Chen, Hao Chen, and Pheng-Ann Heng. "Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/96.
Full textAbstract:
Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions. Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal. In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations. Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction. Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space. A domain critic module (DCM) is set up for discriminating the feature space of both domains. We optimize the DAM and DCM via an adversarial loss without using any target domain label. Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.
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Yang, Zhuo, Tariq Amin Khan, Wei Li, Hua Zhu, Zhijian Sun, Zhengjiang Zhang, Jincai Du, and Jianxin Zhou. "Thermal-Hydraulic Performance and Optimization of Tube Ellipticity in a Plate Fin-and-Tube Heat Exchanger." In ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipack2018-8448.
Full textAbstract:
The flow field inside the heat exchangers is associated with maximum heat transfer and minimum pressure drop. Designing a compact heat exchanger and employing various techniques to enhance its overall performance has been widely investigated and still an active research field. However, few researches deal with thermal optimization. The application of elliptic tube is an effective alternative to circular tube which can reduce the pressure drop significantly. In this study, numerical simulation and optimization of variable tube ellipticity is studied at low Reynolds numbers. The three-dimensional numerical analysis and a multi-objective genetic algorithm (MOGA) with surrogate modelling is performed. Two row tubes in staggered arrangement in fin-and-tube heat exchanger is investigated for combination of various elliptic ratio (e = minor axis/major axis) and Reynolds number. Tube elliptic ratio ranges from 0.2 to 1 and Reynolds number ranges from 150 to 750. The tube perimeters are kept constant while changing the elliptic ratio. The numerical model is derived based on continuum flow approach and steady-state conservation equations of mass, momentum and energy. The flow is assumed as incompressible and laminar due to low inlet velocity. Results are presented in the form of Colburn factor, friction factor, temperature contours and streamline contours. Results show that increasing elliptic ratio increases the friction factor due increased flow blocking area, however, the effect on the Colburn factor is not significant. Moreover, tube with lower elliptic ratio followed by higher elliptic ratio tube has better thermal-hydraulic performance. To achieve maximum heat transfer enhancement and minimum pressure drop, the Pareto optimal strategy is adopted for which the CFD results, Artificial neural network (ANN) and MOGA are combined. The tubes elliptic ratio (0.2 ⩽ e ⩽ 1.0) and Reynolds number (150 ⩽ Re ⩽ 750) are the design variables. The objective functions include Colburn factor (j) and friction factor (f). The CFD results are input into ANN model. Once the ANN is computed and its accuracy is checked, it is then used to estimate the model responses as a function of inputs. The final trained ANN is then used to drive the MOGA to obtain the Pareto optimal solution. The optimal values of these parameters are finally presented.
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Fang, Yuchun, Zhengyan Ma, Zhaoxiang Zhang, Xu-Yao Zhang, and Xiang Bai. "Dynamic Multi-Task Learning with Convolutional Neural Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/231.
Full textAbstract:
Multi-task learning and deep convolutional neural network (CNN) have been successfully used in various fields. This paper considers the integration of CNN and multi-task learning in a novel way to further improve the performance of multiple related tasks. Existing multi-task CNN models usually empirically combine different tasks into a group which is then trained jointly with a strong assumption of model commonality. Furthermore, traditional approaches usually only consider small number of tasks with rigid structure, which is not suitable for large-scale applications. In light of this, we propose a dynamic multi-task CNN model to handle these problems. The proposed model directly learns the task relations from data instead of subjective task grouping. Due to its flexible structure, it supports task-wise incremental training, which is useful for efficient training of massive tasks. Specifically, we add a new task transfer connection (TTC) between the layers of each task. The learned TTC is able to reflect the correlation among different tasks guiding the model dynamically adjusting the multiplexing of the information among different tasks. With the help of TTC, multiple related tasks can further boost the whole performance for each other. Experiments demonstrate that the proposed dynamic multi-task CNN model outperforms traditional approaches.