Conversion Technique Research Articles

Autonomous Landing Strategy for Micro-UAV with Mirrored Field-of-View Expansion

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker’s center point relative to the MAV’s body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs.

An estimation method for vision-based autonomous landing system for fixed wing aircraft

In this paper, we propose a vision-based estimation method for autonomous landing of fixed-wing aircraft for Advanced Air Mobility. The concept of autonomous flight with minimal human intervention has gained significant interest with a particular focus on autonomous landing. The goal is to overcome the limitations of existing instrument landing systems and reduce reliance on GNSS during aircraft landing. The proposed system leverages the following techniques for high-performance and real-time operations in various real-world environments and during all stages of landing. A deep learning segmentation model was directly learned, created, and used for robust runway recognition performance against environmental changes around the runway. Algorithms were designed to adapt to different flight stages considering the varying information offered by image data when the aircraft is in mid-air and on the ground. The relative lateral position from the aircraft to the runway was estimated using bird’s-eye-view conversion and inverse projection techniques instead of conventional perspective-n-point methods for reducing the recognition error occurring from the conversion between 2D image and 3D world. Finally, the mixed-precision technique was used for the segmentation model to improve inference speed and ensure real-time operation in real-world deployment. Estimation stability and reliability were improved by using a Kalman Filter to cope with sensor uncertainties caused by the real-world flight environment. Consequently, we show that the average error in the lateral position estimation by the proposed method is comparable to the accuracy of a single GNSS and demonstrate successful autonomous landing of our test aircraft with a set of flight tests.

Spiritual care competences of healthcare workers in emergency and intensive care-aprospective questionnaire study

In intensive and emergency care, patients and their relatives are confronted with potentially existential crises. Spiritual care can be an additional resource to address related psychosocial and physical symptoms and to support patients and their relatives. Accordingly, healthcare workers need spiritual skills to recognize and respond to these needs. What spiritual competencies do healthcare workers in intensive and emergency care have? Are there differences between professions and genders? What factors influence spiritual competencies? The prospective questionnaire study included physicians participating in intensive care and emergency medicine courses and nurses who were training or working in intensive and emergency care. Self-reported spiritual competencies were assessed using the Spiritual Care Competence Questionnaire (SCCQ), which captures the following areas: perceptual competence, team-spirit, documentation competence, self-awareness and proactive opening, knowledge about other religions, competence in conversation techniques and proactive empowerment-competence. We included 465 physicians (50% female, years in profession: mean = 4.0, standard deviation [SD] = 3.5) and 86nurses (80% female, years in profession: mean = 12.7, SD = 10.7). The average SCC was2.3 (SD 0.4) out of amaximum of 4points, with higher spiritual competences among spiritual and religious respondents. There were differences in specific competencies between the professions and genders. Women indicated ahigher level of competence in the area of perception and conversation skills, physicians in documentation skills. Overall, there is aclear need to train healthcare staff in the field of intensive care and emergency medicine.

A review on the heterogeneous catalyst for effective conversion of lignocellulosic biomass

This study examines a review of the heterogeneous catalyst for effective conversion of lignocellulosic biomass. In light of the challenges posed by a growing global population and the depletion of natural resources, there is an urgent need for alternative solutions. One such solution is the conversion of biomass into fuel, which has the potential to be a cost-effective and widely available substitute for fossil fuels. Solid catalysis and a range of chemical conversions effectively convert biomass into value-added chemicals. A critical factor in this process is the development of a highly efficient and selective catalysis system, along with the necessary chemical reactions. This review provides a comprehensive discussion of the various sources of biomass and their essential chemical composition. It also covers the conversion of biomass into a value-added chemical and the indispensable role of catalysis in the conversion process. Additionally, the review explores various conversion techniques for transforming biomass into useful forms of energy, and highlights their potential applications. By providing a detailed analysis of this process, this review aims to contribute constructively to the ongoing search for sustainable and effective solutions to energy challenges.

A fundus vessel segmentation method based on double skip connections combined with deep supervision.

Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice. This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase. Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

MBSFC: hyperspectral image classification based on multi-branch and spectral feature conversion

ABSTRACT Hyperspectral image classification (HSI) is a vital aspect of remote sensing technology. However, the high-dimensional nature of HSI and the continuous spectral bands significantly impact its classification performance. In this paper, we propose a classification method called MBSFC for HSI classification. MBSFC utilizes multi-branch and spectral feature conversion (SFC) techniques to eliminate redundant information and extract valuable features. Firstly, to preserve spectral-spatial information, the SFC block is introduced to eliminate redundant information through up-sampling. Subsequently, the feature maps from the SFC block and the spectral bands reduced by a factor of four are fed into three branches: the spectral branch, spatial-X branch, and spatial-Y branch. Each branch employs a 3D-CNN-based dense block and attention mechanism to extract useful features. Finally, the obtained features from the three branches are fused for HSI classification. To cater to different application scenarios, we divide MBSFC into three models with different network structures: MBSFC-s, MBSFC-m, and MBSFC-l. The MBSFC-l model, with a three-branch structure, achieves the best performance. The three models differ in the number of branches in the feature extraction. Experimental results on four publicly available hyperspectral datasets show that MBSFC achieves competitive results with small samples compared to other state-of-the-art methods. The code is available at https://github.com/TeresaTing/MBSFC.

Advancing automobile dry clutch fault diagnosis through innovative imaging techniques and Vision transformer integration

The study investigates the significance of clutch condition monitoring in automotive transmissions to preempt mechanical failures, enhance efficiency, and mitigate risks to human safety and maintenance costs. It explores the integration of Vision Transformer (ViT) with imaging techniques, such as scalograms, spectrograms, polar plots, radar plots, and Hilbert-Huang transforms, to diagnose faults in dry friction clutches. By transforming vibration signals into image representations and utilizing ViT for fault classification, the study aims to identify the most effective imaging technique and optimal hyperparameters for accurate fault diagnosis. Experimental studies on a test rig with varying fault conditions demonstrate the effectiveness of ViT in diagnosing clutch faults when coupled with different image conversion techniques. The results highlight the potential of integrating spectrogram image processing with ViT, achieving a 100% accuracy in fault diagnosis for clutch systems, thus advancing the analysis of faults in clutch systems.

A Complete Review on DC-to-DC Converter Topologies for Energy Sustainable Electro-mobility under Environmentally Heterogeneous Power Conditions

The electric vehicle is an upcoming technology that upgrades the biosphere and diminishes pollution across the globe. Electric Vehicles powered by batteries mitigate the problem of the emission of greenhouse gases and air pollution. Research has been undertaken lately to integrate sources of renewable energy to electrify the E-Vehicle to reduce the dependency on fossil fuels, making it an eco-friendly and nil carbon emission transport system. The increased usage and the forecasted growth of E- -vehicles urge the research to be centered on power electronic converters to attain highly efficient, cost-effective, and reliable charging infrastructure for the vehicle battery. The pivot necessity is to provide electricity to the E-vehicle efficiently and continuously. Apart from battery and grid voltage levels, electric vehicles have several systems operating at a variety of power levels. This makes the role of DC/DC converters inevitable in EV operation. This paper presents various DC/DC converter topologies that are employed in EV charging and power conversion techniques. Basic topology and working are first described along with recent developments and variants of basic topologies are also presented.

High-efficiency mid-infrared CW-seeded optical parametric generation in PPLN waveguide

Mid-infrared optical sources have been extensively used in a variety of applications, including gas sensing and metrology, by exploiting the fingerprint fundamental vibrational absorption bands of molecules in this spectral region. Parametric frequency conversion techniques, such as optical parametric generation (OPG) and optical parametric oscillator (OPO), represent common methodologies for mid-infrared generation. Notably, continuous-wave (CW)-seeded OPG exhibits superior stability and performance compared to conventional OPG. Here, we present a simple method for mid-infrared source generation based on femtosecond OPG in periodically poled lithium niobate (PPLN) waveguides. In addition to its robustness and simplicity, mid-infrared CW-seeded OPG features a high quantum conversion efficiency of 46.5% and a low threshold. The phase of CW-seeded OPG was passively referenced to the CW laser, making it suitable for dual-comb applications. This system shows great promise in the miniaturization of mid-infrared combs and will be applied to non-laboratory applications, including open-path atmospheric gas sensing and portable environmental monitoring.

The Conversion of Unicompartmental Knee Arthroplasty to Total Knee Arthroplasty with Non-CT Based Robotic Assistance: A Novel Surgical Technique and Case Series.

Robotic-assisted devices help provide precise component positioning in conversion of unicompartmental knee arthroplasty (UKA) to total knee arthroplasty (TKA). A few studies offer surgical techniques for CT-based robotic-assisted conversion of UKA to TKA, however no studies to date detail this procedure utilizing a non-CT based robotic assisted device. This paper introduces a novel technique employing a non-CT based robotic assisted device (ROSA® Knee System, Zimmer Biomet, Warsaw, IN) for converting UKA to TKA with a focus on its efficacy in gap balancing. We present three patients (ages 46 to 66) who were evaluated for conversion of UKA to TKA for aseptic loosening, stress fracture, and progressive osteoarthritis. Each patient underwent robotic-assisted conversion to TKA. Postoperative assessments at 6 months revealed improved pain, function, and radiographic stability. Preoperative planning included biplanar long leg radiographs to determine the anatomic and mechanical axis of the leg. After arthrotomy with a standard medial parapatellar approach, infrared reflectors were pinned into the femur and tibia, followed by topographical mapping of the knee with the UKA in-situ. The intraoperative software was utilized to evaluate flexion and extension balancing and plan bony resections. Then, the robotic arm guided placement of the femoral and tibial guide pins and the UKA components were removed. After bony resection of the distal femur and proximal tibia, the intraoperative software was used to reassess the extension gap, and plan posterior condylar resection to have the flexion gap match the extension gap. The use of a non-CT based robotic assisted device in conversion of UKA to TKA is a novel technique and a good option for surgeons familiar with robotic-assisted arthroplasty, resulting in excellent outcomes at 6 months.

How to talk to relatives about corneal donation

Corneal donation counselling is an important part of the process to inform and support potential donors and their relatives. In this article various aspects of the conversation techniques on corneal donation are discussed, including raising awareness of the importance of donation, clarifying questions and concerns, emphasizing the potential of donation and considering the emotional burden on the relatives of potential donors. Also emphasized is the role of the counsellor as atrustworthy and empathetic contact who can help family members of potential donors make an informed and positive decision. It is pointed out that aprofessional and empathetic approach to corneal donation can help to increase the willingness to donate and ultimately that adonation can help visually impaired or blind people regain their sight and quality of life through donation.

Predicting long-term deposit customers using convolutional neural network and data conversion technique

Predicting long-term deposit customers using convolutional neural network and data conversion technique

Development of high flux photocatalytic agcl-cooh-mwcnt/pes ultrafiltration membranes for enhanced waste water treatment and fouling mitigation

A structurally stable AgCl-COOH MWCNT/PES composite membrane was designed and fabricated by combining phase conversion and post-treatment techniques, which can solve the serious membrane fouling in industrial wastewater treatment. The carboxylation of MWCNTs not only enhanced their dispersion but also facilitated their binding with silver chloride, resulting in a stable composite structure that significantly improved contaminant degradation capabilities. The post-treatment technique increased the membrane’s water purification flux by 150 % (reaching 520 L·m2·h−1·bar−1) while maintaining a high BSA retention rate of 95 %. Additionally, the composite membrane demonstrated excellent photocatalytic antifouling properties, with a flux recovery rate of 91.8 % under UV irradiation, and exhibited more than 99 % inhibition of Escherichia coli. This method provides a scalable solution for enhancing the performance of PES membranes in wastewater treatment applications.

Lignocellulose hydrothermal artificial humic acid production: Reaction parameters screening and investigation of model/real feedstock

Utilizing relatively mild hydrothermal conversion technique (under optimized conditions, 200 °C, 4 h, 1:10 solid-liquid ratio, 0.5 M NaOH), lignocellulosic biomass can be efficiently transformed into humic acids, thereby significantly enhancing its utilization. Additionally, this approach provides a means for precise manipulation of the composition and properties of the resulting humic acids. In this comprehensive study, lignocellulosic biomass, comprising various attributes like coniferous, broadleaf, and grassy species, as well as model components like cellulose, hemicellulose, and lignin, underwent hydrothermal reactions to produce humic acids. Firstly, a systematic evaluation was conducted to determine the influence of operational parameters on the formation of humic acids. Subsequently, the characteristics of humic acids derived from diverse lignocellulosic biomass sources were analyzed and benchmarked against commercial humic acids. Based on the empirical findings, a mechanistic pathway for the generation of humic acids was figured out. The results indicate that the primary stages in the hydrothermal conversion of lignocellulosic biomass to humic acids involve initial hydrolysis into smaller molecular components, followed by further reactions leading to the formation of humic acids. Notably, the interplay between cellulose, hemicellulose, lignin, and other minor components, such as tannins and pectins, plays a pivotal role in the synthesis of humic acids. The characterization of the derived humic acids revealed several noteworthy distinctions from commercial products. In the infrared spectroscopy (FTIR) and elemental analysis (EA) characterization, the synthetic humic acid has more oxygen functional groups, alkane groups and aromatic structures. In the X-ray photoelectron spectroscopy (XPS), the synthetic humic acid has more diverse forms and proportions of elements. In thermogravimetric analysis (TGA), the synthesized humic acid has higher thermal stability. The synthetic humic acid has a rougher surface microstructure. The synthetic humic acid has higher fluorescence intensity in three-dimensional fluorescence spectrum (3D EEM). Lignocellulose biomass raw materials with high wood quality content can obtain higher humic acid yield, but the interaction of cellulose, hemicellulose, lignin and other group components (such as tannins, pectin, etc.) in the hydrothermal process plays an important role in the generation of humic acid. This research offers valuable insights into the chemically driven production of humic acids and the strategic control of their properties, based on the structural and compositional nuances of the raw materials.

Experimental concerning the influence upon gaseous emissions by using HHO addition to an LPG powered Otto engine

Abstract Because of the increased pollution regulations imposed on internal combustion engines across the world as well to preserve the air quality, effective solutions for new and older cars must be found in reference to their emission levels. This paper focuses on the impact of adding oxyhydrogen (HHO) fueled into an Otto engine working with liquefied petroleum gas (LPG), in reference to the emission concentration values. HHO is produced using a water electrolytic conversion technique that works in tandem with the Otto engine. The electrolytic convertor can generate 1,800 litters of HHO gas from 1 liter of water. Due to this addition, the total calorific power of the fuel mixture is growing by around 30 % in comparison to only LPG fueled situation, considered as basic one. All measurements were conducted in a laboratory with accredited and approved gas analyzers and measuring devices. The study presents comparative results of the basic measurements, at an engine speed of 800 rotations per minute (RPM) and respectively at 2,000 RPM, when using an LPG as fuel, respectively a mixture LPG and HHO. As conclusion, one proves that the addition of HHO can generate as effect the decreased of particular emission values.

Combustion and co-combustion of biochar: Combustion performance and pollutant emissions

Biomass is a renewable and clean energy source, offering the potential to reduce fossil fuel consumption and greenhouse gas emissions when it is used as a biofuel. However, the disadvantages of raw biomass such as poor grindability, high moisture content, and low calorific value hinder its widespread industrial combustion. Thermochemical conversion techniques are widely used to convert biomass to biochar, which serves not only as an adsorbent and soil amendment but also as solid fuels alternative to fossil energy. The upgraded biochar exhibits better physicochemical properties and improved combustion performance. This paper focuses on the properties and combustion behavior of biochar as a solid fuel, reviewing common production techniques of biochar and their effects on yields and properties of biochar. It also examines the combustion characteristics and kinetics of biochar in single- and co-combustion scenarios with other fuels, such as coal, sludge, and petroleum coke, analyzing the influencing factors of combustion behavior and kinetic parameters. This study further investigates the emission characteristics during the combustion of biochar, including gaseous pollutants (such as CO, NOx, SOx, and HCl), ash-related issues, and particulate matter (PM) emissions. Additionally, a new fuel type named “bioslurry”, which is developed with biochar as a main material, is introduced. Furthermore, this paper discusses the techno-economic and environmental aspects of biochar application, exploring the feasibility of its industrial production and utilization.

Enabling High-Power Conditioning and High-Voltage Bus Integration Using Series-Connected DC Transformers in Spacecrafts

This article proposes a photovoltaic power processor for high-voltage and high-power distribution bus, between 300 V and 900 V, to be used in future space platforms like large space stations or lunar bases. Solar arrays with voltages higher than 100 V are not available for space application, being necessary to apply power conversion techniques. The idea behind this is to use series-connected zero-voltage and zero-current unregulated and isolated DC converters to achieve high bus voltage from the existing solar arrays. Bus regulation is then achieved through low-frequency hysteretic control. Topology description, semiconductor selection, design procedure, simulation and experimental validation, including tests in vacuum and partial pressures, are presented.

Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy’s optimal actions. We verified RIVC’s effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc.

Towards the production of 1,4:3,6-dianhydro-α-d-glucopyranose from biomass fast pyrolysis based on oxalic acid-assisted torrefaction pretreatment

Pyrolysis, a promising thermochemical conversion technique, can convert lignocellulosic biomass into various value-added products. Herein, the selective production of 1,4:3,6-dianhydro-α-d-glucopyranose (DGP) was achieved through fast pyrolysis of cellulose based on oxalic acid-assisted torrefaction (OAT) pretreatment. The mixture of cellulose and oxalic acid first underwent torrefaction, followed by fast pyrolysis to yield DGP. Simultaneously, the air atmosphere was considered to facilitate the formation of esterification intermediates, thereby promoting the formation of DGP. The effects of torrefaction temperature, oxalic acid amount, as well as pyrolysis temperature were carefully explored for DGP production. A yield of 9.6 wt% and a selectivity of 57.5 % for DGP were achieved from the micro-scale pyrolysis of cellulose under the optimized conditions. In lab-scale tests, the DGP yield reached 12.5 wt% from cellulose. The OAT-based pyrolysis was confirmed to be applicable to the waste cellulose-rich materials, with a DGP yield of 8.2 wt% from waste cotton and 6.0 wt% from spent paper in lab-scale experiments. Additionally, the OAT-based pyrolysis also showed the potential for poly-generation of DGP and formic acid at different stages.

Emerging Bismuth-Based Materials: From Fundamentals to Energy Applications

In practical applications, supercapacitors, renewable and efficient techniques of energy storage and conversion, are commonly assembled into supercapacitor, which has been attracting growing interest in energy applications for the past decade. To achieve this decisive task, it is essential that electrode materials with high energy density and electrochemical stability are explored for environment-friendly and economical utilization. Numerous studies have been conducted on metal-based compounds and their relative materials; including bismuth-based materials and their composites are promising because of their redox behavior, charge storage capacity, environmental friendliness, and increasing research toward their application in energy for solar water splitting and supercapacitor. Herein, we outline bismuth materials and their composites, as investigated by our research, highlight their applications in energy, and, more importantly, focus on the study of their performance in photoelectrochemical hydrogen production and supercapacitive electrochemical techniques. We also present a summary of energy storage devices and their mechanisms, as well as other types of non-bismuth-based electrode materials available in the market. In addition, the major challenges and future perspectives of bismuth-based composites for energy purposes are addressed.