A Low-Cost and Ultralight Unmanned Aerial Vehicle-Borne Multicamera Imaging System Based on Smartphones

Newly developed oblique photogrammetry (OP) techniques based on unmanned aerial vehicles (UAVs) equipped with multicamera imaging systems are widely used in many fields. Smartphones cost less than the cameras commonly used in the existing UAV OP system, providing high-resolution images from a built-in imaging sensor. In this paper, we design and implement a novel low-cost and ultralight UAV OP system based on smartphones. Firstly, five digital cameras and their accessories detached from the smartphones are then fitted into a very small device to synchronously shoot images at five different perspective angles. An independent automatic capture control system is also developed to realize this function. The proposed smartphone-based multicamera imaging system is then mounted on a modified version of an existing lightweight UAV platform to form a UAV OP system. Three typical application examples are then considered to evaluate the performance of this system through practical experiments. Our results indicate that both horizontal and vertical location accuracy of the generated 3D models in all three test applications achieve centimeter-level accuracy with respect to different ground sampling distances (GSDs) of 1.2 cm, 2.3 cm, and 3.1 cm. The accuracy of the two types of vector maps derived from the corresponding 3D models also meet the requirements set by the surveying and mapping standards. The textural quality reflected by the 3D models and digital ortho maps (DOMs) are also distinguishable and clearly represent the actual color of different ground objects. Our experimental results confirm the quality and accuracy of our system. Although flight efficiency and the accuracy of our designed UAV OP system are lower than that of the commercial versions, it provides several unique features including very low-cost, ultralightweight, and significantly easier operation and maintenance.

The anisotropic broadband surface plasmon polariton and hot carrier properties of borophene monolayer

Borophene monolayer with its intrinsic metallic and anisotropic band structures exhibits extraordinary electronic, optical, and transport properties. Especially, the high density of Dirac electrons enables promising applications for building low-loss broadband SPP devices. However, a systematic characterization of the surface plasmon polariton (SPP) properties and hot carriers generated from the inevitable SPP decay in borophene has not been reported so far. Most importantly, the mechanism for SPP losses remains obscurely quantified. In this work, from a fully first-principles perspective, we explicitly evaluate the main loss effects of SPP in borophene, including the Drude resistance, phonon-assisted intraband and direct interband electronic transitions. With this knowledge, we further calculate the frequency- and polarization-dependent SPP response of borophene, and evaluate some typical application-dependent figure of merits of SPP. On the other hand, we evaluate the generation and transport properties of plasmon-driven hot carriers in borophene, involving energy- and momentum-dependent carrier lifetimes and mean free paths, which provide deeper insight toward the transport of hot carriers at the nanoscale. These results indicate that borophene has promising applications in next-generation low-loss optoelectronic devices and photocatalytic reactors.

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Environmental scientists often face the challenge of predicting a complex phenomenon from a heterogeneous collection of datasets that exhibit systematic differences. Accounting for these differences usually requires including additional parameters in the predictive models, which increases the probability of overfitting, particularly on small datasets. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to incorporate information about the structure of the dataset explicitly. To this end, we look at a typical application in remote sensing: the estimation of leaf area index of white winter wheat, an important indicator for agronomical modeling, using measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach show that Bayesian hierarchical models are a versatile tool for the prediction of leaf area index, particularly in scenarios where the available data sources are heterogeneous.

MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks

Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of researches have come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable medical knowledge-assisted mechanism (MKA) is proposed in this paper. The mechanism is aimed at assisting general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, and food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-BERT-GPT achieves state-of-the-art performance.

Trusted Service Evaluation for Mobile Edge Users: Challenges and Reviews

With the increasing growth of web services shared in various mobile edge platforms, it becomes necessary to evaluate all the candidates based on their quality of services to reduce the users’ service selection cost. However, the service quality data released by service providers cannot be simply deemed as trusted due to various subjective or objective reasons, which further produce a series of serious trust-aware service evaluation problems, including service quality data sparsity and lack of feedback incentive. In view of this, we summarize the challenging issues existing in the current research field of trusted mobile edge service evaluation. Afterward, we review the current research status of the trusted service evaluation in the mobile edge environment and discuss one of the typical application scenarios based on trusted service evaluation, that is, recommender systems, as well as their diverse categories. We believe this research could be helpful in assisting a mobile edge platform to build a trusted reputation system for various smart applications hosted in the mobile edge platform.

Seismic-while-drilling by drill-bit source and large-aperture ocean-bottom array

Seismic while drilling (SWD) by drill-bit source has been successfully used in the past decades and is proven using variable configurations in onshore applications. The method creates a reverse vertical seismic profile (RVSP) dataset from surface sensors deployed as arrays in the proximity of the monitored wells. The typical application makes use of rig-pilot reference (pilot) sensors at the top of the drill-string and also downhole. This approach provides while-drilling checkshots as well as multioffset RVSP for 2-D and 3-D imaging around the well and prediction ahead of the bit. For logistical (sensor deployment) and cost (rig time related to technical installation) reasons the conventional drill-bit SWD application is typically much easier onshore than offshore. We present a novel approach that uses a network of passive-monitoring sea bottom nodes pre-deployed for microseismic monitoring to simultaneously and effectively record offshore SWD data. We study the results of a pilot test where we passively monitored the drilling of an appraisal well at the Wisting discovery in the Barents Sea with an ocean-bottom cable deployed temporarily around the drilling rig. The continuous passive recording of vibration signals emitted during the drilling of the well provides the SWD data set, which is treated as a reverse vertical seismic profile. The study is performed without rig-pilot signal. The results are compared with legacy data and demonstrate the effectiveness of the approach and point to future applications for real-time monitoring of the drilling progress, both in terms of geosteering the drill bit and predicting formation properties ahead of the bit by reflection imaging.

A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings

Square rebars were developed and used for decades in the early development of reinforced concrete (RC) structures; however, the objectives of modern concrete structure durability analyses and standards are centered on round rebars in past decades, which are not suited for RC buildings utilizing square rebars. Considering the absence of proper evaluation techniques to evaluate the square rebar RC structures’ durability accurately, a novel durability prediction method has been proposed for this type of historical building. The method is based on major parts as in-situ investigation, finite element model simulation, component importance analysis, and structural durability prediction. The durability prediction calculation method was established on the experimental results of the realistic historical concrete tests and corrosion-induced cover cracking experiments for square rebar components. It was found that the carbonization-resistant ability of historical concretes was relatively weaker than that of current concretes and the calculation method for critical corrosion depth of square rebar was different from that of round rebar. Furthermore, two typical application cases are presented to introduce the procedure of the method in detail. Consequently, the research outcomes can be directly used on the durability prediction and protection works for historical RC buildings.

Study on Product Opinion Analysis for Customer Satisfaction on E-Commerce Websites

The E-commerce websites have been emerged in a high range of marketing benefits for the users to publish or share the experience of the received product by posting review that contain useful comments, opinions and feedback on the product. These days, a large number of clients acquire freedoms to look at comparative items in online sites and pick their top choices in computerized retailers, like Amazon.com and Taobao.com. Client audits in online media and electronic trade Websites contain important electronic word data of items. Sentiment Analysis is broadly applied as voice of clients for applications that target showcasing and client care. Sentiment extractors in their most essential structure classify messages as either having a good or negative or once in a while neutral supposition. A typical application of sentiment investigation is the programmed assurance of whether an online review contains a positive or negative review. Subsequently, in this paper, with the use of the strategies on sentiment analysis, obstinate sentences alluding to a particular element are first recognized from item online audits. We have proposed deep learning strategy as a classification model for discovering the condition of review. The outcomes showed suggested site for the client dependent on the early audits, past reviews and answer given to inquiry audit for the client. Additionally, it is seen that the proposed strategy can ready to answer every one of the reviews with a superior closeness like a human reaction to the client.

Computational modelling of mechanical response of glulam beams due to moisture inducted delamination

Glued laminated timber beams are nowadays used as load-bearing beams of large-span structures that operate in various humidity conditions. Typical application areas are aqua parks with high humidity as well as market halls with low humidity. It is related to the possibility of the occurrence of cracks typical for the drying of wood, even with such controlled conditions of production technology as glued laminated timber. Cracks visible on the used girders raise doubts as to the safe operation of the structures. The subject of this paper is the computational simulation and the evaluation of the influence of beam delamination on the mechanical response of the structure. The attention was established on a typical two-span beam of constant height with a slight slope to the horizontal. The numerical analysis was carried out for three variants of the location of potential delamination of different scope. The beams were modeled as a problem of the linear theory of elasticity in a plane stress state with orthotropic material properties. The calculations were made in the Abaqus software environment. The results obtained in the paper allow to determine the areas in which the presence of delamination or cracks should be considered dangerous from the point of view of the safety of operation. Computational analysis is helpful in assessing the safety of structures where cracks appear. Theoretical considerations are supplemented by an example from engineering practice.

An investigation on fronthaul and millimeter wave technologies for 5G

Abstract.The growing demand for communications and the emergence of new applications have put the capacity, transmission rate and latency of communications systems to a more severe test. In this context, 5G communication technology has emerged. 5G applications are divided into three typical application scenarios: eMBB, URLLC and mMTC. In order to support these applications, the key technologies of 5G need to be studied in depth. This paper firstly investigates the networking architecture of 5G bearer networks, and carries out a detailed analysis and comparison of 5G fronthaul technology, including the optical fibre direct connection solution, passive WDM solution, active WDM/OTN solution and WDM-PON solution. Secondly, this paper introduces 5G millimetre wave technology to achieve large capacity and high spectral efficiency transmission, including direct intensity modulation method, external modulation method, optical heterodyne method, optical injection locking method and optical phase-locked loop method. Finally, this paper provides an outlook on 5G fronthaul technology and millimetre wave technology.