A theoretical analysis is presented to show the general occurrence of phase clusters in weakly, globally coupled oscillators close to a Hopf bifurcation. Through a reductive perturbation method, we derive the amplitude equation with a higher-order correction term valid near a Hopf bifurcation point. This amplitude equation allows us to calculate analytically the phase coupling function from given limit-cycle oscillator models. Moreover, using the phase coupling function, the stability of phase clusters can be analyzed. We demonstrate our theory with the Brusselator model. Experiments are carried out to confirm the presence of phase clusters close to Hopf bifurcations with electrochemical oscillators.
Abstract This study presents a novel approach to predict a complete source to destination trajectory of a vehicle using a partial trajectory query. The proposed architecture is scalable to extremely large-scale data with respect to the dense road network. A deep learning model Long Short Term Memory (LSTM) has been used for analyzing the temporal data and predicting the complete trajectory. To handle a large amount of data, clustering of similar trajectory data is used that helps in reducing the search space. The clusters based on geographical locations and temporal values are used for training different LSTM models. The proposed approach is compared with the other published work on the parameters as Average distance error and one step prediction accuracy The one-step prediction accuracy is as good as 81% and Distance error are .33 Km. Our proposed approach termed Clustered LSTM is outperforming in both the parameters when compared with other reported results. The proposed solution is a clustering-based predictive model that effectively contributes to accurately handle the large scale data. The outcome of this study leads to improvise the navigation systems, route prediction, traffic management, and location-based recommendation systems.
Corrosion processes mainly affect the probability of failure, which then leads to consequences, such as, fire, explosion, or environmental damage. This paper focuses on the use of Bayesian network models for assessing the probability of corrosion. The Bayesian network approach incorporates cause- effect relationships of complex systems in the form of conditional probabilities. This method considers both knowledge uncertainties (i.e., modeling uncertainties) and data uncertainties to make more informed decisions. The Bayes theorem allows the model to predict the probability of events from their causes, and, if a particular event is known to have occurred, predict probable causes of that event. Two case studies, the first one involving internal corrosion and the second involving external corrosion of oil and gas pipeline, are presented, along with validation using field measurements. The extension of the approach to predicting stress corrosion cracking of pipelines is discussed.
Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment. The ability of the systems to detect any behavior that can lead to disruption can help in alerting the system beforehand and prevent its harmful effects. Many machine learning techniques have already been in use at large tokamaks like JET and ASDEX, but are not suitable for ADITYA, which is comparatively small. Through this work, we discuss a new real-time approach to predict the time of disruption in ADITYA tokamak and validate the results on an experimental dataset. The system uses selected diagnostics from the tokamak and after some pre-processing steps, sends them to a time-sequence Long Short-Term Memory (LSTM) network. The model can make the predictions 12 ms in advance at less computation cost that is quick enough to be deployed in real-time applications.
Generating medical reports manually is a difficult task, especially in rural areas and in urgent medical cases, where there is an emergency. It can also be error-prone for inexperienced physicians to generate a medical report. There are various deep learning methodologies such as Image captioning, image classification that has been implemented earlier to solve this problem. Generating a medical report automatically is a difficult task, considering the less amount of open-source data available and the paired data which contains medical Images and the report is also limited. One of the challenging tasks is data bias in medical Imaging. A generative encoder-decoder model is suggested to solve this problem in an efficient way. There are various other challenges. First, the medical report itself contains various heterogeneous information such as paragraphs, tags, keywords. Secondly, it is also difficult to identify the abnormal regions in medical images. To solve this problem, a multi-task framework is built, which can perform tag generation and paragraph generation. LSTM (Long Short Term Memory) is built to generate long heterogeneous paragraphs in the medical report. The model working is demonstrated on Chest X-Ray dataset and also on pathology dataset.
A coordinated experimental and modeling study of selected factors influencing intergranular corrosion (IGC) penetration depth into AA5083 is presented. Potentiostatic tests at -0.73 VSCE in 0.6 M NaCl solution were conducted on LT, ST, and LS surfaces of sensitized AA5083. The IGC penetration rate was found to depend on applied potential, degree of sensitization (DoS), exposure time, and propagation direction relative to the rolling direction. Depths of IGC penetration were characterized through metallographic techniques, microscopy, and image analyses. The results were analyzed to describe the IGC depth distributions and to serve as input data for a set of descriptive models that can estimate IGC damage progression for AA5083 during exposure to 0.6 M NaCl at pH 8.3. This set of descriptive models was validated with 94% accuracy for 100oC sensitization. Extension of this combined experimental and modeling approach to 80oC sensitization demonstrated that sensitization temperature is another significant factor.
Human-Computer Interaction (HCI) brings about colossal measures of information-bearing possibilities for understanding a human client's aims, objectives, and wants. Realizing what clients need and need is a key to shrewd framework help. The hypothesis of psyche idea known from concentrates in creature conduct is embraced and adjusted for an expressive client displaying. Speculations of the brain are theoretical client models speaking to, somewhat, a human client's musings. A hypothesis of the spirit may even uncover unsaid information. Along these lines, client displaying becomes information disclosure going past the human's information and covering explicit space experiences. Speculations of the psyche are incited by mining HCI information. Information mining ends up being an inductive demonstrating. Insightful collaborator frameworks are inductively demonstrating a human client's aims, objectives, and so forth, just as space information is, essentially, learning frameworks. To adapt to the danger of failing to understand the situation, learning frameworks are furnished with the expertise of reflection. Here we proposed Gesture recognition; Gesture recognition is a developing theme in the present advancements. The fundamental focal point of this is to perceive the human motions utilizing numerical calculations for Human-Computer cooperation. Just a couple of methods of Human-Computer Interaction exist, are: through the console, mouse, contact screens, and so on. Every one of these gadgets has its restrictions with regards to adjusting more flexible equipment in computers. Motion recognition is one of the basic methods to fabricate easily to use interfaces. Typically motions can be started from any substantial movement or state; however, they normally begin from the face or hand. Signal recognition empowers clients to interface with the gadgets without truly contacting them. This Chapter depicts how hand signals are prepared to play out specific activities like exchanging pages, looking up or down on a page.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
