Data-driven affective product design using complete three-dimensional surface data

As the main part of design display and evaluation, product three-dimensional (3D) form is the core object in affective product design. However, previous research has not yet addressed the development of technical models and method involving complete 3D surface data, and thus cannot guarantee the quality of affective product design. By using the techniques of triangular mesh model, spherical harmonic and conditional variational auto-encoder, this paper proposes a data-driven affective product design method composed of several technical models using complete 3D surface data. These models include: mathematical model for quantifying 3D form, recognition model for recognizing customer’s affective responses, and generative model for generating new 3D forms. For affective product design, the mathematical model achieves the acquisition and processing of complete 3D surface data, the recognition model improves the objectivity and accuracy of recognition by integrating the 3D form data into the calculation process of emotion recognition, and the generative model realizes the automatic generation of new 3D forms in response to emotional data based on the recognition results. Each model provides technical support for realizing the acquisition, processing and generation of complete 3D surface data of product form, and ensures the systematic and completeness of the proposed method for the affective product design involving 3D form innovation. The feasibility of the method is verified by an example of car design, and the results show that it is an effective affective product design method involving 3D form innovation.

Hydrogeomorphology as a Tool in the Evolutionary Analysis of the Dynamic Landscape—Application to Larrodrigo, Salamanca, Spain

Evolutionary analysis of the fluvial landscape provides relevant inputs for the environmental management of a territory, in such a way that the understanding of the dynamics of fluvial spaces becomes a preponderant factor in the definition of protection and management strategies. Although the development of geographic information systems is a step forward in the study of the landscape, it is necessary to establish methodological frameworks that make remote sensing techniques available at multiple spatio-temporal scales, especially in basins with high levels of intervention. In the present study, we develop a methodology for the analysis of the fluvial landscape development in the last century of a highly modified water body, through the detailed study of hydrogeomorphic elements, using remote sensing techniques including high-density surface data (LiDAR) and historical aerial imageries; when supported by fieldwork, these allow for the identification of the sequence of sedimentation–erosion zones, differentiating in detail the zones denominated as areas of current erosion, accretion zones, and historical erosion zones. An application of the methodology was carried out in the Larrodrigo stream, located in Salamanca, Spain.

Generation of a 3D model of the inside volume of shoes for e-commerce applications using industrial X-ray computed tomography

In 2018, 47 % of global internet users had purchased footwear products through the internet, making it the second most popular online shopping category worldwide right after clothing with 57 %. In the same year, on average, about every sixth parcel delivered in Germany (16.3 %) was returned. With the effort and costs that are associated with the return of shoes, the objective of reducing the number of returns for shoes promises an enormous economic potential and helps to reduce the CO2 emissions due to a lower trafic volume. This paper presents a workflow for determining the inside volume surface of shoes using industrial X-ray computed tomography (CT). The fundamental idea is based on the Region Growing (RG) method for the segmentation of the shoe's inner volume. Experiments are performed to illustrate the correlation of image quality and segmentation result. After obtaining the 3D surface model of an individual foot, the inner volume surface data of a scanned shoe can then be registered and evaluated in order to provide a reliable feedback for the customer regarding the accuracy of fit of a shoe and the individual foot on the basis of an overall "metric of comfort" before buying online. This step is not part of the work at hand. Conclusions are drawn and suggestions for improving the robustness and the exibility of the workflow are given, so it can be adapted to various shoe types and implemented in a fully automated measurement process in the future.

Field Assessment of Camera Based Drilling Dynamics

Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.

Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations

Methane emissions in Canada have both anthropogenic and natural sources. Anthropogenic emissions are estimated to be 4.1 Tg a−1 from 2010–2015 in the National Inventory Report submitted to the United Nation's Framework Convention on Climate Change (UNFCCC). Natural emissions, which are mostly due to boreal wetlands, are the largest methane source in Canada and highly uncertain, on the order of ∼ 20 Tg a−1 in biosphere process models. Aircraft studies over the last several years have provided “snapshot” emissions that conflict with inventory estimates. Here we use surface data from the Environment and Climate Change Canada (ECCC) in situ network and space-borne data from the Greenhouse Gases Observing Satellite (GOSAT) to determine 2010–2015 anthropogenic and natural methane emissions in Canada in a Bayesian inverse modelling framework. We use GEOS-Chem to simulate anthropogenic emissions comparable to the National Inventory and wetlands emissions using an ensemble of WetCHARTS v1.0 scenarios in addition to other minor natural sources. We conduct a comparative analysis of the monthly natural emissions and yearly anthropogenic emissions optimized by surface and satellite data independently. Mean 2010–2015 posterior emissions using ECCC surface data are 6.0 ± 0.4 Tg a−1 for total anthropogenic and 11.6 ± 1.2 Tg a−1 for total natural emissions. These results agree with our posterior emissions of 6.5 ± 0.7 Tg a−1 for total anthropogenic and 11.7 ± 1.2 Tg a−1 for total natural emissions using GOSAT data. The seasonal pattern of posterior natural emissions using either dataset shows slower to start emissions in the spring and a less intense peak in the summer compared to the mean of WetCHARTS scenarios. We combine ECCC and GOSAT data to characterize limitations towards sectoral and provincial-level inversions. We estimate energy + agriculture emissions to be 5.1 ± 1.0 Tg a−1, which is 59 % higher than the national inventory. We attribute 39 % higher anthropogenic emissions to Western Canada than the prior. Natural emissions are lower across Canada. Inversion results are verified against independent aircraft data and surface data, which show better agreement with posterior emissions. This study shows a readjustment of the Canadian methane budget is necessary to better match atmospheric observations with lower natural emissions partially offset by higher anthropogenic emissions.

New Advances in Surface Data Logging Technologies for Comprehensive Real-Time Petrophysical Evaluation to Optimize Logging Programs in a Mature Field; A Case History, Onshore, Abu Dhabi

The challenges of drilling new wells are increasingly associated with minimizing HSE risks, that relate to chemical radioactive sources in the Bottom Hole Assembly for formation evaluation. Drilling risks such as differential sticking, also necessitates investigation of alternative petrophysical data gathering methodologies that can fulfil these requirements. Surface Data Logging presents a viable alternative in mature fields, satisfying petrophysical data gathering and interpretation in real-time as well, as traditional geological applications and offset well correlations in a way, to optimize well construction costs. During the planning phase, a fully integrated approach was adopted including advanced cutting and advanced gas analysis to be deployed, in this case study, well together with experienced well site personnel. A comprehensive pre-well study was conducted reviewing all offset nearby wells data. The workflow included provision of full real-time advanced cuttings and gas analysis for formation evaluation and reservoir fluid composition, lithology description, and addressing effective hole cleaning concerns. The advanced Mud Logging services was run in parallel to the Logging While Drilling services for a few pilot wells, in order to correlate downhole tool parameters, with respect to data quality control, to identify the petrophysical character of the formation markers for benchmarking future data gathering requirements. In addition to the potential use of standalone fully integrated advanced Mud Logging to reduce risks and minimize field development costs. With the help of experienced wellsite geologist on location and real time advanced gas detection utilizing high resolution mass spectrometer and X-Ray fluorescence (XRF) and X-Ray Diffraction (XRD) data, geological boundaries and formations tops were accurately identified across the whole drilled interval. Modern and advanced interpretation techniques for the integrated analysis were proven to be effective in determining sweet spots of the reservoir, fluid type, and overall reservoir quality. Deployment of fully integrated mud logging solutions with new interpretation methodologies can be effective in providing a better understanding of reservoir geological and petrophysical characteristics in real-time, offering viable alternative for minimizing formation evaluation sensors in the BHA, particularly eliminating radioactive sources, while reducing overall developments costs, without sacrificing formation evaluation requirements.

Simulation of severe storms of tornadic intensity over Indo-Bangla region

Many severe thunderstorms of tornadic intensity were reported in the northwestern parts of Bangladesh during 30 August to 14 September, 2008. Two among them occurred at Nilphamari and Kurigram districts on 30th August, and at Nilphamari district on 3rd September. The tornadic storms are studied based on a field survey, surface data, radar and satellite observations and model simulations. Low level moisture influx by southerly flow from the Bay of Bengal coupled with an upper level westerly jet stream causing intense instability and shear in the wind fields triggered a series of storms for two weeks. The exact time and locations of the storms are investigated by using the hourly precipitation data retrieved from a S-band radar of Bangladesh Meteorological Department (BMD) located at Dhaka. Subsequently, the storms are simulated by using the WRF-ARW model on double nested domains at 9 and 3 km horizontal resolutions based on 6 hourly FNL analyses and boundary conditions of NCEP. Among the typical characteristics of the storms, the CAPE, Storm-Relative Environment Helicity (SREH), Bulk Richardson Number Shear (BRNSHR), dew point depression, and potential vorticity are studied. Results show that while there are differences of 2-3 hours between the observed and simulated time of the storms, the distances between observed and simulated locations of the storms are several tens of kilometers. The maximum CAPE is generally above 2400 J kg-1. The maximum amount of vorticity transferred by directional shear in the storm updraft (helicity) due to convective motion simulated by the model is 766 m2 sec-2, and the highest value of BRNSHR that define the region in which low-level mesocyclogenesis is more likely is 168 m2 sec-2 among the 2 cases, which is generally supposed to produce rotating storms according to the prescribed range.

Multimodal Fusion of Face and Palm Using Local Color Binary Patterns and Haralick Features

Face recognizable proof has drawn in numerous scientists because of its novel benefit, for example, non-contact measure for include obtaining. Varieties in brightening, posture and appearance are significant difficulties of face acknowledgment particularly when pictures are taken as dim scale. To mitigate these difficulties partially many exploration works have been completed by considering shading pictures and they have yielded better face acknowledgment rate. A strategy for perceiving face utilizing shading nearby surface highlights is depicted. Test results show that Face ID approaches utilizing shading neighborhood surface highlights astonishingly yield preferred acknowledgment rates over Face acknowledgment approaches utilizing just shading or surface data. Especially, contrasted and grayscale surface highlights, the proposed shading neighborhood surface highlights can give great coordinating with rates to confront pictures taken under extreme varieties in enlightenment and furthermore for low goal face pictures. The other biometric framework utilizes palmprint as quality for the recognizable proof and validation of people. The principal point is to extract Haralick highlights and utilization of probabilistic neural organizations for confirmation utilizing palmprint biometric quality. PolyUdatabase tests are taken from around 200 clients every client's 2 examples are gained. This palm print biometric recognizes the phony (fake) palmprint made of POP (Plaster of paris) and separates among living and non-living dependent on the entropy highlight. Test results portray that the eleven Haralick feature values are acquired in execution stage and productive precision is accomplished.

A statistical study of pre-monsoon weather over south Bengal using descriptive and inferential techniques

Statistical data summarization techniques, curve fitting methods and statistical tests, both parametric and non-parametric, have been applied to form a comprehensive idea about pre-monsoon weather over South Bengal situated in the northeastern part of the Indian sub-continent. The work is based on surface data recorded at 15 major observatories during the period 1969-2000, spread across the western plateau and highlands, Rarh and Gangetic region of South Bengal. The homogeneity of pre-monsoon rainfall and its variability over different stations has been studied using Bartlett`s and Kruskal Wallis tests. For stations with complete weather information for at least 30 consecutive years, time series analysis has been carried out on rainfall data for a climatological overview of long-term behaviour, seasonal and cyclical fluctuations of pre-monsoon rainfall in those areas. This paper, apart from being a regional study, highlights the use of certain parametric and non-parametric tests, not so widely used in the context of climatology.