Generation Method Research Articles

Insulator Defect Detection Based on YOLOv5s-KE

To tackle the issue of low detection accuracy in insulator images caused by intricate backgrounds and small defect sizes, as well as the requirement for real-time detection on embedded and mobile devices, this research introduces the YOLOv5s-KE model. Integrating multiple strategies, YOLOv5s-KE aims to boost detection accuracy significantly. Initially, an enhanced anchor generation method utilizing the K-means++ algorithm is proposed to generate more appropriate anchor boxes for insulator defects. Moreover, an attention mechanism is integrated into both the backbone and neck networks to enhance the model’s capacity to focus on defect features and resist interference. To improve the detection of small defects, the EIoU loss function is implemented in place of the original CIoU loss function. In order to meet the real-time detection needs on embedded and mobile devices, the model is further refined through the integration of Ghost convolution for lightweight feature extraction and a linear transformation to reduce the computational burden of standard convolution. A channel pruning strategy is deployed to optimize the sparsely trained network, diminishing redundancy, and improving model generalization. Additionally, the CARAFE operator replaces the original upsampling operator to minimize model parameters and elevate detection speed. Experimental outcomes demonstrate that YOLOv5s-KE achieves a detection accuracy of 92.3% on the Chinese transmission line insulator dataset, marking a 5.2% enhancement over the original YOLOv5s. The streamlined version of YOLOv5s-KE achieves a detection speed of 94.3 frames per second, indicating an improvement of 30.1 frames per second compared to the original model. Model parameters are condensed to 9.6 M, resulting in a detection accuracy of 91.1%. This study underscores the precision and efficiency of the proposed approach, suggesting that the advanced strategies explored introduce novel possibilities for insulator defect detection.

Methods for generation, recommendation, exploration and analysis of scholarly publications

Methods for generation, recommendation, exploration and analysis of scholarly publications

Design of pulse width modulation circuit based on PWM modulation

Abstract The key to the modulation technology of the switching power supply is to control the on-off of the power switch and realize the real-time adjustment of the duty cycle with the system through the control signal. In order to improve the realization accuracy of the control signal, a generation method different from the traditional pulse width modulation signal is proposed. The two edge signals are processed by the RS trigger to generate the pulse width modulation signal. The circuit is designed and simulated in SMIC 0.18 μm CMOS process. In the full feedback current range, the duty cycle of the pulse width modulation signal decreases linearly, which can effectively improve the conversion efficiency of the power supply.

A modified experimental approach for generating internal solitary waves using the dual paddle technique

A modification to the dual paddle wave generation method for internal solitary waves (ISWs) has been achieved through the implementation of the adjusted high-order unidirectional (aHOU) model and the Miyata–Choi–Camassa (MCC) model. This modified method utilizes layer-averaged horizontal velocities from ISWs in both the upper and lower fluid layers to control the operational velocities of the corresponding paddles. Experimental investigations conducted in a two-layer fluid with varied stratification conditions were employed to evaluate the applicability of the aHOU and MCC models. The validation of this approach involved examining the stability of generated ISWs by comparing experimental waveforms with theoretical predictions, demonstrating good agreement with prior research. Furthermore, evaluation of the modified wave generation method included an analysis of dimensionless phase velocity and characteristic frequency. The study also explored the quasi-linear relationship between the designed wave amplitude and the actual wave amplitude, establishing a polynomial fit between the actual wave amplitude and the layer thickness ratio. This approach offers a new method for stable and controllable laboratory generation of ISWs. Under finite water depth conditions, the method can produce ISWs that propagate stably over long distances and effectively control parameters such as wave amplitude and wave profile across a broad range of wave amplitudes and stratifications. These results confirm the effectiveness of the modified method and underscore its practical applicability in ISWs generation.

Machines, psychology, and hypothesis generation: Commentary on Banker et al. (2024).

Advances in machine learning and artificial intelligence are revolutionizing many aspects of human life, and as Banker et al. (2024) illustrate, generative artificial intelligence may also facilitate hypothesis generation in academic research. But while it is easy to imagine this idea generating some alarm (i.e., hypothesis generation may seem like the most creative, human part of research), their work actually raises an even more important question: Why should we believe that the current (human) method of hypothesis generation is somehow ideal in the first place? This article discusses the implications of their work and outlines how automated content analysis and machine learning can also help researchers determine what hypotheses deserve attention in the first place. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design and modeling of PV-integrated Double Skin Facades and application to retrofit buildings

Double Skin Façade (DSF) system comprises two glazing layers with a ventilated cavity. Integrating photovoltaic (PV) modules within the outer layer of DSFs offers an efficient method for electricity generation. Current tools for modeling and analyzing DSF systems are complex and resource-intensive, lacking the capability to evaluate the performance of innovative PV-DSF systems during the early design stage. This study develops a mathematical model to evaluate the electrical and thermal performance of PV-DSF systems, considering architectural design elements such as PV color and relative orientation. Based on an energy balance approach, the model is particularly suited for designing PV-DSF systems in heritage buildings, which often have color and relative orientation constraints. The model is applied to assess the performance of PV-DSF systems with conventional clear glass PV and colored front glass PV modules under the climatic conditions of Montreal, Canada. Results indicated that conventional clear glass PV module exhibit higher PV cell temperature than colored PV modules due to greater transmissivity, with peak temperature differences at noon of 5.5 °C, 6.2 °C, and 6.5 °C for orange, blue, and gray PV modules, respectively. On the contrary, the influence of PV's color front glass on room air temperature is non-significant. Furthermore, the optimal orientation for maximum energy yield is not always south-facing; it depends on the hourly distribution of the beam, diffuse solar irradiation, and ambient air temperature. For Montreal, west-facing DSFs produce more electrical and thermal energy on a summer design day because the hourly distribution of beam radiation is skewed towards afternoon hours.

DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ( μ -maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free μ -map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived μ -maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating μ -maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on contrast-free CMR are desirable to overcome the limitations associated with contrast enhancement. We introduce a novel deep generative learning method, termed cine-generated enhancement (CGE), which transforms standard contrast-free cine CMR into LGE-equivalent images for MI assessment. CGE features with multislice spatiotemporal feature extractor, enhancement contrast modulation, and sophisticated loss function. Data from 430 patients with acute MI from 3 centers were collected. After image quality control, 1525 pairs (289 patients) of center I were used for training, and 293 slices (52 patients) of the same center were reserved for internal testing. The 40 patients (401 slices) of the other 2 centers were used for external testing. The CGE robustness was further tested in 20 normal subjects in a public cine CMR data set. CGE images were compared with LGE for image quality assessment and MI quantification regarding scar size and transmurality. The CGE method produced images of superior quality to LGE in both internal and external data sets. There was a significant (P<0.001) correlation between CGE and LGE measurements of scar size (Pearson correlation, 0.79/0.80; intraclass correlation coefficient, 0.79/0.77) and transmurality (Pearson correlation, 0.76/0.64; intraclass correlation coefficient, 0.76/0.63) in internal/external data set. Considering all data sets, CGE demonstrated high sensitivity (91.27%) and specificity (95.83%) in detecting scars. Realistic enhancement images were obtained for the normal subjects in the public data set without false positive subjects. CGE achieved superior image quality to LGE and accurate scar delineation in patients with acute MI of both internal and external data sets. CGE can significantly simplify the CMR examination, reducing scan times and risks associated with gadolinium-based contrasts, which are crucial for acute patients.

Study of ultrasonic parameters for quality monitoring of plate heat exchanger

Abstract Heat exchangers are crucial heat transfer devices widely used in energy industries such as power generation, oil production, and engine cooling. The plate heat exchanger is one of the most economical and efficient types of exchangers. It transfers heat using metal plates and has a high heat transfer efficiency because the fluid is exposed to a higher plate surface area. However, due to the presence of dissolved or suspended particles in the heat-exchanging fluids, a layer of deposit (fouling layer) is formed on the heat exchanger plates. It results in reduced efficiency of the device. Thus, a common cleaning method, such as chemical-based or manual methods, is employed. However, it always poses a risk of over-cleaning or removal of the protective oxide layer. Thus, an efficient mechanism to monitor the onset of the fouling layer would greatly help to achieve increased efficiency and cost reduction in the cleaning of the plates. Recent advances show ultrasound as one of the promising non-destructive inspection techniques to monitor the growth of the fouling layer. However, the variation in heat-exchanging fluid temperature affects the investigated acoustical parameter measurement. Thus, this research aims to study the linear and nonlinear ultrasonic parameters incorporating temperature-dependent effects from the fluid to provide an accurate real-time monitoring tool. The nonlinear acoustics analysis is experimentally employed in the research using the Second harmonic generation method (SHGM). The proposed research will help design ultrasound-assisted plate heat exchangers for maximum heat transfer efficiency.

Detection of cyberattacks using bidirectional generative adversarial network

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.

An Efficient Progressive Grid Generation Method Considering Internal Quality and Boundary Grid Adjustment

Due to its elegant appearance and high structural efficiency, free-form grid structures are increasingly adopted in architectural design. However, it is a challenging task for engineers to generate a uniform and well-shaped grid on a free-form surface while considering the processing of both interior and boundary of grid, especially for composite surfaces. To generate well-shaped grids with uniform rods, regular cells and smooth visual effects over free-form surfaces, this paper develops an innovative triangular grid generation method capable of automatically managing grid boundary with extending initial surface. In the method, nodes of grid structure are considered to be zero mass particles and are progressively added to the extended surface from geometric center to the boundary of surface. A boundary-processing algorithm is then established to evenly distribute nodes on initial boundary curve, while ensuring that grid cells at the boundary do not exhibit elongation. A mechanical simulation system based on spring-mass model is improved to optimize spatial grid structure, rods of grid are regarded as linear springs, a surface attraction force and an anchoring force are performed on grid nodes. Moreover, the proposed method allows architects change grid direction and rods length easily, which can greatly improve the efficiency of design. Several case studies show that the method can effectively avoid distortion of grids and generate well-shaped grids that can meet aesthetic requirements.

Population Generation Methods for Metaheuristic Algorithms Used to Construct Compact Fuzzy Classifiers of Medical Data

Population Generation Methods for Metaheuristic Algorithms Used to Construct Compact Fuzzy Classifiers of Medical Data

Optimal Launch Manifesting for Space Freight Forwarders

This paper proposes a novel framework to optimize a launch vehicle manifesting plan for small satellites from the perspective of a space freight forwarder. The forwarder in space logistics coordinates individual shipment orders from the launch customers and launch service providers like a freight forwarder in ground logistics does. An optimal launch manifesting problem is formulated as a variant of the vehicle routing problem involving the decisions on carriers, launch strategies (dedicated or multi-payload), launch dates, and satellite deployment sequences. A solution procedure based on the column generation method is proposed to address the challenges associated with the large-scale combinatorial optimization problem. A case study demonstrates the effectiveness of the proposed framework and solution procedure.

Semantic-Aware Dynamic Generation Networks for Few-Shot Human-Object Interaction Recognition.

Recognizing human-object interaction (HOI) aims at inferring various relationships between actions and objects. Although great progress in HOI has been made, the long-tail problem and combinatorial explosion problem are still practical challenges. To this end, we formulate HOI as a few-shot task to tackle both challenges and design a novel dynamic generation method to address this task. The proposed approach is called semantic-aware dynamic generation networks (SADG-Nets). Specifically, SADG-Net first assigns semantic-aware task representations for different batches of data, which further generates dynamic parameters. It obtains the features that highlight intercategory discriminability and intracategory commonality adaptively. In addition, we also design a dual semantic-aware encoder module (DSAE-Module), that is, verb-aware and noun-aware branches, to yield both action and object prototypes of HOI for each task space, which generalizes to novel combinations by transferring similarities among interactions. Extensive experimental results on two benchmark datasets, that is, humans interacting with common objects (HICO)-FS and trento universal HOI (TUHOI)-FS, illustrate that our SADG-Net achieves superior performance over state-of-the-art approaches, which proves its impressive effectiveness on few-shot HOI recognition.

DynER: Optimized Test Case Generation for Representational State Transfer (REST)ful Application Programming Interface (API) Fuzzers Guided by Dynamic Error Responses

Modern web services widely provide RESTful APIs for clients to access their functionality programmatically. Fuzzing is an emerging technique for ensuring the reliability of RESTful APIs. However, the existing RESTful API fuzzers repeatedly generate invalid requests due to unawareness of errors in the invalid tested requests and lack of effective strategy to generate legal value for the incorrect parameters. Such limitations severely hinder the fuzzing performance. In this paper, we propose DynER, a new test case generation method guided by dynamic error responses during fuzzing. DynER designs two strategies of parameter value generation for purposefully revising the incorrect parameters of invalid tested requests to generate new test requests. The strategies are, respectively, based on prompting Large Language Model (LLM) to understand the semantics information in error responses and actively accessing API-related resources. We apply DynER to the state-of-the-art fuzzer RESTler and implement DynER-RESTler. DynER-RESTler outperforms foREST on two real-world RESTful services, WordPress and GitLab with a 41.21% and 26.33% higher average pass rate for test requests and a 12.50% and 22.80% higher average number of unique request types successfully tested, respectively. The experimental results demonstrate that DynER significantly improves the effectiveness of test cases and fuzzing performance. Additionally, DynER-RESTler finds three new bugs.

Gearbox fault diagnosis based on RGT-MFFIN and multi-sensor fusion image generation

Abstract In gearbox fault diagnosis based on vibration and torque state data, traditional one-dimensional time-frequency domain analysis methods often suffer from insufficient feature expression and mining, and require complex noise reduction and filtering preprocessing. To address this issue, this paper proposes a fusion image generation method that integrates the advantages of recurrence plot (RP) and Gramian angular summation field (GASF) to generate recurrence Gramian transformed (RGT) images. This approach integrates both global and local fault information, making the fault characteristics more intuitive and easier to analyze. Given that multi-sensor collaboration can enhance feature representation, feature-level fusion increases the computational burden, and decision-level fusion is prone to losing inter-sensor correlation information, this paper adopts data-level fusion for image sample enhancement. In the diagnostic method, the challenge of traditional convolutional neural networks (CNNs) in extracting diverse geometric linear structures from fused images is addressed by introducing deformable convolutional blocks for initial feature extraction. Additionally, a multi-scale feature fusion interaction network (MFFIN) is constructed. This network incorporates a channel-space interactive attention mechanism on top of multi-scale feature extraction, assigning weights to features according to their importance while facilitating the interaction of feature information. Finally, validation is carried out using public datasets, and the experimental results show that the proposed method demonstrates significant advantages in classification accuracy and robustness under variable operating conditions and noise, thereby proving its effectiveness and practicality.

Planar and non-planar hybrid printing trajectory generation method

Abstract Aiming at the problem of step effect, a planar and non-planar hybrid printing trajectory generation method is studied. The region with the inclination of the surface and horizontal plane of the model is extracted for non-planar printing, which can improve the surface printing quality of the model while controlling the printing time. The experimental results show that the proposed algorithm has good adaptability and can meet the requirements of engineering applications.

Data-driven robust optimisation of hydrogen infrastructure planning under demand uncertainty using a hybrid decomposition method

In the race towards “Net-zero”, hydrogen has emerged as one of the key alternatives to carbon-based fossil fuels for a sustainable decarbonisation. This work studies the spatially explicit multi-period hydrogen infrastructure planning under demand uncertainty that contributes to the heat decarbonisation in Great Britain. Demand uncertainty surrounding future hydrogen supply chains poses challenges to cost optimisation and system security, so uncertainty-resilient policies are required to ensure robust operations. In this work, we employ data-driven robust optimisation to develop a framework for uncertainty-aware representative days explicitly characterised by polyhedral uncertainty sets. The proposed framework is applied on a multi-period mixed-integer linear model with dual temporal resolution which aims to determine the optimal yearly investment decisions and hourly operational decisions for the hydrogen infrastructure planning under demand uncertainty. To efficiently solve the large-scale two-stage adaptive robust optimisation problem, a hybrid decomposition algorithm is developed based on a two-step hierarchical procedure and the column-and-constraint generation method, which can significantly reduce the computational complexity. The optimisation results highlight how uncertainty can result in the total cost increase, and verify the advantages on controlling solution conservatism in the adaptive robust optimisation compared to the static robust optimisation.

Enhancing hydrovoltaic power generation with carbon spheres coated on nickel foam/polyethylene terephthalate substrate

The present study aims to assess the viability of hydrovoltaic impact as a potential method for energy generation. This methodology utilizes the capillary action of water in devices composed of nickel (Ni) foam/polyethylene terephthalate (PET) substrates coated with hydrothermally synthesized carbon spheres at various temperatures (180, 200, and 220 °C). The prepared materials and devices were analyzed using a range of analytical techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared spectroscopy (FTIR). The SEM images revealed the spherical morphology of the carbon particles and their presence on Ni foam, whereas the XRD patterns indicated that the carbon spheres were amorphous. Spectroscopic analysis revealed the presence of hydroxyl functional groups in all the samples. The performance of the devices was assessed by electrochemical tests, including open-circuit potential (OCP), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). The device coated with carbon spheres synthesized at 180 °C demonstrated enhanced performance with an elevated OCP value of 0.077 V, increased surface charge area, lower resistance to water, and reduced charge transfer resistance. These findings indicate the successful production of hydrovoltaic energy by utilizing the capillary action of water in carbon-sphere-coated Ni foam/PET devices.

FINANCIAL AND ECONOMIC PROSPECTS FOR CREATING GREEN ENERGY PROJECTS IN THE UKRAINE-POLAND COLLABORATION

This paper examines the economic and financial advantages of improving international collaboration between Ukraine and Poland in the development of alternative energy projects. It conducts a thorough evaluation of existing and forthcoming renewable energy generation technologies, using expert, Delphi, and methods based on statistics to identify the most promising and universally applicable solutions. The calculation of the payback period for small-scale green energy projects pays particular attention to the actual duration of the period in the case of small business ventures. The findings are summarized with a vision for the future Ukrainian-Polish cooperation in the scope of green energy cooperation and specific instruments and processes that could contribute to the success of the collaboration.The article outlines the potential approach that involves the isolation of the cooperation between Ukraine and Poland in green energy relying on the creation of investment and venture funds, public support for the manufacturing of a renewable energy component, and the establishment of non-volatile industrial complexes. Additionally, measures such as independent small alternative power plants and the initiatives for the development of the Ukrainian-Polish joint network of alternative energy are mentioned.The current war between Ukraine and Russia has drawn attention to the crucial need for energy security and diversification, making Ukraine-Poland collaboration on green energy projects all the more important. Ukraine may minimize its reliance on the Russian energy supply by focusing on renewable energy programs, while Poland can invest in sustainable energy to boost regional stability and economic progress. The article points out that the continuation and advancement of green energy initiatives require international cooperation. The paper offers detailed guidance on how the financial, economic, and technological opportunities and relations between Ukraine and Poland may be enhanced.