Evaluating Global Navigation Satellite System (GNSS) Constellation Performance for Unmanned Aerial Vehicle (UAV) Navigation Precision

Accurate localization is paramount for unmanned aerial vehicles (UAVs) spanning various technical and industrial domains, necessitating a comprehensive assessment of global navigation satellite system (GNSS) precision. This study investigates the performance of distinct GNSS constellations in determining the precise location of a building utilizing a high-precision GNSS receiver. The receiver, incorporating advanced multi-frequency and full-constellation positioning capabilities, was integrated with a smartphone via Bluetooth to enable the UAV’s acquisition of centimeter-level positioning data. Sequential utilization of single satellite systems—such as GPS-only, GLONASS-only, Galileo-only, SBAS-only, and BeiDou-only—facilitated the documentation of latitude and longitude coordinates for the designated building. Subsequent comparison of these coordinates with a specialized Geographic Information System (GIS) was conducted to evaluate their positional accuracy. The comparative analysis underscores significant variability in the precision offered by each satellite constellation, providing valuable insights for optimizing UAV navigation across GIS, IoT, construction, and other sectors requiring high-precision localization. This research underscores the significance of high-precision GNSS receivers in enhancing UAV-based geospatial assessments, emphasizing the critical selection of appropriate satellite systems for tailored localization tasks. The study contributes to advancing UAV navigation strategies, ensuring robust and accurate geospatial data collection within diverse operational frameworks.

Precision bending of high-quality components for volume applications

Recent new technology developments were presented in the field of industrial bending operations,including flexible stretch forming and 3D rotary stretch forming.Attempts were made to give an overview of different mechanisms that influence dimensional accuracy,including local cross-sectional deformations such as suck-in and volume conservation effects,along with global deformations such as springback.An analytical model was developed to determine the particular influence of different material,geometry and process parameters on dimensional variability of bent components.The results were discussed in terms of overall process capability(Cp) and associated process windows.The results show that different governing mechanisms prevail in various bending operations,meaning that attention has to be placed on controlling those process parameters that really are important to part quality in each specific case.Several strategies may be defined for reducing variability.One alternative may be to design more robust process and tool technology that reduce the effect of upstream parameters on dimensional variability of the formed part.The results show that optimal tool design and technology may in specific cases improve the dimensional accuracy of a formed part.Based on the findings discussed herein,it is concluded that advances in industrial bending operations require focus on improving the understanding of mechanical mechanisms,including models and parameter development,new technology developments,including process,tool,measurement and control capabilities,and process discipline at the shop floor,combined with a basic philosophy of controlling process parameters rather than part attributes.

Effect of deformation temperature on precipitation, microstructural evolution, mechanical and corrosion behavior of 6082 Al alloy

The influence of cryorolling(CR),room temperature rolling(RTR)and post annealing on precipitation,microstructuralevolution(recovery,recrystallisation and grain growth),mechanical and corrosion behavior,was investigated in the present work.The precipitation kinetics and microstructural morphology of CR,RTR,and post annealed samples were investigated by differentialscanning calorimetry(DSC),transmission electron microscopy(TEM),and electron back scattered diffraction(EBSD)to elucidatethe observed mechanical properties.After annealing at200°C,UTS and hardness of CR samples(345MPa and HV127)wereimproved as compared to RTR samples(320MPa and HV115).The increase in hardness and UTS of CR samples after annealing at200°C was due to precipitation ofβ''from Al matrix,which imparted higher Zener drag effect as compared to RTR samples.Theimprovement in corrosion and pitting potentials was observed for CR samples(?1.321V and?700mV)as compared to RTRsamples(?1.335V and?710mV).In CR samples,heavy dislocation density and dissolution of Mg4Al3Si4-precipitates in the Almatrix have improved corrosion resistance of the alloy through formation of protective passive layer and suppression of galvanic cell,respectively.

Material selection of polymeric composite automotive bumper beam using analytical hierarchy process

Selection of materials,as an area of design research,has been under considerable interest over the years.Materials selection is one of the most important activities in the product development process.Inappropriate decision of materials can cause the product to be reproduced or remanufactured.To avoid this circumstance,one of the useful tools that can be employed in determining the most appropriate material is analytical hierarchy process(AHP).To illustrate the application of AHP,six different types of composite materials were considered.The most appropriate one for suitability of use in manufacturing automotive bumper beam was determined by considering eight main selection factors and 12 sub-factors.The AHP analysis reveals that the glass fibre epoxy is the most appropriate material because it has the highest value(25.7%,mass fraction) compared with other materials.The final material is obtained by performing six different scenarios of the sensitivity analysis.It is proved that glass fibre epoxy is the most optimum decision.

U-model Enhanced Dynamic Control of a Heavy Oil Pyrolysis/Cracking Furnace

This paper proposes a case study in the control of a heavy oil pyrolysis/cracking furnace with a newly extended U-model based pole placement controller(U-PPC). The major work of the paper includes: 1) establishing a control oriented nonlinear dynamic model with Naphtha cracking and thermal dynamics; 2) analysing a U-model(i.e., control oriented prototype) representation of various popular process model sets; 3)designing the new U-PPC to enhance the control performance in pole placement and stabilisation; 4) taking computational bench tests to demonstrate the control system design and performance with a user-friendly step by step procedure.

Impulsive efects on global stability of models based on impulsive diferential equations with “supremum” and variable impulsive perturbations

Sufficient conditions are investigated for the global stability of the solu tions to models based on nonlinear impulsive differential equations with ＂supremum＂ and variable impulsive perturbations. The main tools are the Lyapunov functions and Razu mikhin technique. Two illustrative examples are given to demonstrate the effectiveness of the obtained results.

High Density Plasma Nitriding of Al-Cu Alloys for Automotive Parts

Duralumin alloys have been utilized as structural components and parts for aircrafts, train-cars and so forth. Their high specific strength was attractive to those applications; however, their little corrosion resistance and low wear endurance became a fatal demerit in practical applications. In order to overcome these issues of high strength aluminum alloys, high density plasma nitriding is proposed as an effective surface treatment for duralumin. This process has a capability to control the RF- and DC-plasmas independently for nitriding. This enables us to temporally control and describe the plasma state by in-situ plasma diagnosis. This plasma diagnosis was instrumented to search for optimum processing condition to plasma nitriding the duralumin alloys of type A2011. Both type A2011 aluminum alloy plates and pipes were employed to describe the inner nitriding behavior for hardening the duralumin alloys by the present plasma nitriding.

Development of an Algorithm for a Triangular Approach to the Sphere Based on the Platonic Solids Using MATLAB Scripts

The sphere is a common object in uncountable engineering problems, which not only appears in structural elements like domes but also in thousands of mechanisms normally used in diverse kinds of machines. To design, calculate and analyze the behaviour on service of spherical elements, it is essential to have a good method to create an ordered group of discrete points of the spherical surface from the parametric equations commonly used to define the sphere continuously. One of the best known and widely used in high-level programming environment is MATLAB. The programming language has thousands of functions, lots of them specially designed for engineering processes. One of these functions generates a sphere knowing a given radius and shows the result. Nevertheless, this function is really imprecise because it is based on parallels and meridians besides the obtained vertices do not keep a constant distance each other. This causes the fact that it would be appropriate to design a new function to generate accurate discrete approximations of the sphere. The objective of this paper is to create a low-level function in MATLAB to obtain a discrete sphere with high regularity and high approximation in order to provide a good base to solve sphere-based engineering problems. To ensure a perfect symmetry and high regularity platonic bodies, MATLAB will be used as a base to divide the continuous spherical surface in a finite number of regular triangles. The obtained results for the different seed bodies will be represented graphically and compared to each other. The accuracy of each method will be evaluated and compared too.

Plasma Nitriding-Assisted Printing of Stainless Steel Punches for Micro-piercing Process

MEMS （micro-electric-mechanical-system） required for miniature, thin mechanical parts as a structural member; e.g., the miniature pumping system consisted of ten to twelve thin metallic plates before joining. At present, those thin shaped sheets were fabricated by the chemical etching. Their geometric inaccuracy as well as long leading time often became an engineering issue. Micro-piercing process was expected to make mass production of thin sheet products with complex and accurate geometry for much shorter leading time once the die for this micro-piercing was built in. In the present paper, a new plasma nitriding-assisted printing was proposed as an automatic production line to fabricate the micro-piercing punch. After preparation of CAD-data of the punch head, its two dimensional geometry was ink-jet printed directly on the AISI420 stainless steel die-substrate. The unprinted surface area was only plasma nitrided at 693 K for 14.4 ks to transform this two dimensional micro-pattern to the three dimensional hardness distribution in the AISI420 substrate. Through the mechanical removal of ink-jet printed area, the flat punch head with sharp edge comers was fabricated in much shorter duration time than the end-milling. SEM-EDX, surface profiling measurement as well as micro-hardness testing were employed to describe each step in the above plasma printing. The thin MEMS stainless steel part with a micro-pendulum as well as three S-letter shaped springs was taken for an example to describe this automatic production procedure of plasma printing from the CAD data mining to the micro-piercing punch finishing.

A new adaptive bending method using closed loop feedback control

As the competition from companies in low cost countries increases,the need for more automated production which reduces labour cost while improving product quality is required.A new rotary compression bending set-up with automated closed-loop feedback control is thus being developed.By transferring in-process measurement data into an algorithm for predicting springback and bend angle prior to the unloading sequence,the dimensional accuracy is improved.This work focuses on the development of this steering model.Since the method used does not increase cycle time,it is attractive for high-volume industrial applications.More than 150 bending tests of AA6060 extrusions were conducted to determine the capability of the technology.The results show that by activating the automated closed-loop feedback system,the dimensional accuracy of the bent parts is more than three times better than that obtained by traditional compression bending.Since the steering model permits the direct use of additional process data,such as instant wall thickness and cross sectional distortions,it is believed that extension of the measurement capabilities would improve the accuracy of the methodology even further.

串联光伏电池非晶硅薄膜的Nd^(3+):YAG激光辅助掺杂与同时毛化(英文)

对结合激光辅助掺杂和毛化方法合成的光伏电池用Sb掺杂非晶硅薄膜进行了研究。首先,用阈值通量为460mJ/cm2的激光照射镀有200～300nm厚Sb的非晶硅薄膜进行施主扩散。为激活施主,样品用230mJ/cm2的低激光通量再一次进行处理。进行施主激光扩散和活化时,激光光斑的重叠度达到光斑大小的90%,因此随后诱发了薄膜表面的毛化。多晶毛化峰由扫描电子显微镜、喇曼光谱、原子力显微镜等多种表征手段进行了验证,处理后的样品其正面和背面的拉曼光谱均出现了521cm-1的晶体峰值。样品的n型特征得到了Hall效应测试的证实,同时证明了掺杂的有效性。表面毛化粗糙度达到450nm,并且观测到了光电导和吸收光谱的增强,这些均证实了样品光伏性质的改善。

Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring.The scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis.Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server.Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating congestion.However,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario.In our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET.In order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this paper.Besides,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is proposed.Specifically,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning framework.Besides,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency optimization.Simulation results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms.

Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes significant.Motivated by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected vehicle.Aiming at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making mechanism.In addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access network.This paper proposes a novel framework where the bio-inspired mechanism is combined with TOPSIS.In a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global sense.The experimental results confirm that the proposed method performs better when compared with conventional schemes.

Self-Powered,Long-Durable,and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring

Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.

Non-standard tests for process control in chemically bonded sands

Chemically bonded sand cores and molds are more commonly referred to as precision sand systems in the high production automotive powertrain sector. Their behavior in contact with molten metal can lead to casting defects. Consequently, the interaction is of great interest and an important part of metal casting technology. The American Foundry Society(AFS) sand testing is based on physical, mechanical, thermal and chemical properties of the sand system. Foundry engineers have long known that certain AFS sand tests provide limited information regarding control of molding and casting quality. The inadequacy is due to the fact that sand casting processes are inherently thermo-mechanical, thermo-chemical and thermo-physical. Non-standard foundry sand testing has proven useful for laboratory measurement of these characteristics in foundry sand using a disc-shaped specimen. Similarly, the equivalent disc-shaped specimens are used for casting trials. In order to accomplish near-net-shape casting with minimal defects, it is necessary to understand both the properties of the sand system, as well as the interface of molten metal when different binders, additives and/or refractory coatings are used. The methodology for the following non-standard chemically bonded sand tests is described:(1) disc transverse;(2) impact;(3) modified permeability;(4) abrasion;(5) thermal distortion;(6) quick loss on ignition. The data related to the non-standard sand tests were analyzed and interpreted. The test results indicate that there is relatively lower test-to-test variability with the disc-shaped specimens. The non-standard tests were able to discriminate between the chemically bonded polyurethane cold box sand specimens. Further studies should be conducted on various other sand and binder systems as well as on different specimen thicknesses.

Perfect Reconstructable Decimated One-Dimensional Empirical Mode Decomposition Filter Banks

This paper introduces decimated filter banks for the one-dimensional empirical mode decomposition （1D-EMD）. These filter banks can provide perfect reconstruction and allow for an arbitrary tree structure. Since the EMD is a data driven decomposition, it is a very useful analysis instrument for non-stationary and non-linear signals. However, the traditional 1D-EMD has the disadvantage of expanding the data. Large data sets can be generated as the amount of data to be stored increases with every decomposition level. The 1D-EMD can be thought as having the structure of a single dyadic filter. However, a methodology to incorporate the decomposition into any arbitrary tree structure has not been reported yet in the literature. This paper shows how to extend the 1D-EMD into any arbitrary tree structure while maintaining the perfect reconstruction property. Furthermore, the technique allows for downsampling the decomposed signals. This paper, thus, presents a method to minimize the data-expansion drawback of the 1D-EMD by using decimation and merging the EMD coefficients. The proposed algorithm is applicable for any arbitrary tree structure including a full binary tree structure.

Fuzzy-Logic Based Multi-Sensory Quality Evaluation via Communication Network

In this paper, a multi-sensory quality evaluation using an array of instruments to measure different sensory qualities is established via communication network. The network is used to transmit quality data to evaluation computer. And the network-induced delays between instruments and computer may have negative influence on final evaluation results. The main goal of this paper is to analyze network delays’ influence on evaluation results, and present a fuzzy-logic based solution to eliminate the impact and improve the precision of evaluation. And simulations are conducted to show the effectiveness of the proposed approach.

Automatic quantification of morphology on magnetic resonance images of the proximal tibia

The morphological quantification of the proximal tibia of the knee joint is important in knee replacement.Accurate knowledge of these parameters provides the basis for design of the tibial prosthesis and its fixation.Ideally,a prosthesis that is suitable for the morphological characteristics of Chinese knees is needed.In this paper,a deep learning automatic network framework is designed to achieve automatic segmentation and automatic quantitative analysis of magnetic resonance images of the tibia.An enhanced feature fusion network structure is designed,including high and low-level feature fusion path modules to create accurate segmentation of the tibia.A new method of extracting feature points and lines from outline contours of the proximal tibia is designed to automatically calculate six clinical morphological linear parameters of the tibia in real-time.The final result is an automatic visualisation of the tibial contour and automated extraction of tibial morphometric parameters.Validation of the results from our system against a gold standard obtained by manual processing by expert clinicians showed the Dice coefficient to be 0.97,the accuracy to be 0.98,and the correlation coefficients for all six morphological parameters of the automatic quantification of the tibia are above 0.96.The gender-specific study found that the values of the proximal tibial linear parameters of internal and external tibial diameter,anterior and posterior diameter,lateral plateau length,lateral plateau width,medial plateau length,and medial plateau width in male patients are significantly greater than in female patients(all P values<0.01).The results enrich the use of deep learning in medicine,providing orthopaedic specialists with a valuable and intelligent quantitative tool that can assess the progression and changes in osteoarthritis of the knee joint.