Dual Method Research Articles (Page 1)

For a steam heating network (SHN), which is one of the most important parts of the integrated energy systems in an industrial park, it is essential to provide accurate and reliable estimation of hydraulic and thermal states, to ensure its operational safety and economy. Considering two different time-scale characteristics of the hydraulic and thermal processes in the SHN, this article proposes a mechanism and data-driven dual estimation method for the coupling hydraulic-thermal dynamic states. Due to the fact that the existing coupled estimation methods with one time scale may suffer from heavy computational burden, a data-driven dual sequential acrlong DSE method of the SHN is proposed based on the two-time scales, in which the dynamics of slow thermal states can also be captured when performing the acrlong SE of the fast hydraulic process. Furthermore, to improve the computational and communicational efficiency, a distributed interaction strategy based on the nodal transformation matrix is designed for large-scale steam systems. To verify the effectiveness of the proposed method, a single pipeline system and two real-world industrial superheated steam networks are employed. Compared to other state-of-the-art methods, the proposed method achieves the best tradeoff between the estimation accuracy and computational efficiency.

With the surge of data-driven analysis techniques, there is a rising demand for enhancing the exploration of large high-dimensional data by enabling interactions for the joint analysis of features (i.e., dimensions). Such a dual analysis of the feature space and data space is characterized by three components, 1) a view visualizing feature summaries, 2) a view that visualizes the data records, and 3) a bidirectional linking of both plots triggered by human interaction in one of both visualizations, e.g., Linking & Brushing. Dual analysis approaches span many domains, e.g., medicine, crime analysis, and biology. The proposed solutions encapsulate various techniques, such as feature selection or statistical analysis. However, each approach establishes a new definition of dual analysis. To address this gap, we systematically reviewed published dual analysis methods to investigate and formalize the key elements, such as the techniques used to visualize the feature space and data space, as well as the interaction between both spaces. From the information elicited during our review, we propose a unified theoretical framework for dual analysis, encompassing all existing approaches extending the field. We apply our proposed formalization describing the interactions between each component and relate them to the addressed tasks. Additionally, we categorize the existing approaches using our framework and derive future research directions to advance dual analysis by including state-of-the-art visual analysis techniques to improve data exploration.

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this paper, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Methodology for Extracting Low-Frequency Input Impedance of Personal Computer During Operation Using Dual Current Probes Method

Current parametric models have made notable progress in 3D hand pose and shape estimation. However, due to the fixed hand topology and complex hand poses, current models are hard to generate meshes that are aligned with the image well. To tackle this issue, we introduce a dual noise estimation method in this paper. Given a single-view image as input, we first adopt a baseline parametric regressor to obtain the coarse hand meshes. We assume the mesh vertices and their image-plane projections are noisy, and can be associated in a unified probabilistic model. We then learn the distributions of noise to refine mesh vertices and their projections. The refined vertices are further utilized to refine camera parameters in a closed-form manner. Consequently, our method obtains well-aligned and high-quality 3D hand meshes. Extensive experiments on the large-scale Interhand2.6M dataset demonstrate that the proposed method not only improves the performance of its baseline by more than 10% but also achieves state-of-the-art performance. Project page: https://github.com/hanhuili/DNE4Hand.

This paper argues that the digitalisation of university registration processes has given rise to concealed elitism. The phenomenon became apparent during the COVID-19 pandemic, as elite students accessed advanced medical care and education degrees while individuals from indigent families and rural communities bore the brunt of the pandemic. Drawing from the perspectives of critical emancipatory research (CER), the observational study described in this paper sought to explain students' experiences during the transition to digitalised registration processes and to identify practical solutions for the deficiencies in the new system, ensuring inclusivity for all students in the future. This observational study did not have a specific sample size but observed the groups of incoming first-year students gathered outside many university gates during the registration period in the province of KwaZulu-Natal, South Africa. The study consequently concluded that many universities in KwaZulu-Natal are not prepared for digitalisation of student applications for several reasons: (i) the use of technology conceals elitism, a lingering aspect of the apartheid era that disenfranchises students from indigent and rural communities; (ii) universities lack reliable infrastructure, as they were not initially designed for contemporary communication and teaching methods; and (iii) the rise of technological innovation destabilises the intentions of our South African democracy and the massification agenda. This paper proposes a dual method of communication (virtual and walk-in inquiries) that would accommodate democracy and recognise people's right to education. This approach would ensure that students from different socio-economic backgrounds are accommodated in higher education institutions. The paper recommends technological innovations that do not remind black and poor students that they were once marginalised from accessing tertiary education due to systems that were not favourable to them.

Carbene-based bioadhesives have favourable attributes for tissue adhesion, including non-specific bonding to wet and dry tissues, but suffer from relatively weak fracture strength after photocuring. Light irradiation of carbene-precursor (diazirine) also creates inert side products that are absent under thermal activation. Herein, a dual activation method combines light irradiation at elevated temperatures for the evaluation of diazirine depletion and effects on cohesive properties. A customized photo/thermal-rheometer evaluates viscoelastic properties, correlated to the kinetics of carbene:diazoalkane ratios via 19F NMR). The latter exploits the sensitive -CF3 functional group to determine joule-based light/temperature kinetics on trifluoroaryl diazirine consumption. The combination of heat and photoactivation produced bioadhesives that are 3× tougher compared to control. Dual thermal/light irradiation may be a strategy to improve viscoelastic dissipation and toughness of photo-activated adhesive resins.

Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization

Aims: The leaf spot caused by Colletotrichum gloeosporioides (Penz.) Penz &amp; Sacc. is one of the most serious disease in all mango growing regions of the world. In vitro bioassay of fungicides and bioagents were performed to evaluate the effectiveness of different fungicides and bioagents against C. gloeosporioides.&#x0D; Study Design: Poison food techique for fungicides bioassay and dual culture technique for bioagents bioassay.&#x0D; Place and Duration of Study: The laboratory studies were conducted in the Department of Plant Pathology, N. M. College of Agriculture, Navsari Agricultural University, Navsari, Gujarat, India during 2020–2021.&#x0D; Methodology: The poison food technique assessed the efficacy of systemic, contact and combination fungicides against C. gloeosporioides. While the dual culture method was employed to gauge the potency of biocontrol agents, against C. gloeosporioides. &#x0D; Results: In laboratory screening, systemic fungicides such as carbendazim and difenconazole were shown to have 100% growth suppression at 500 ppm. Mancozeb and copper oxychloride, two contact fungicides, demonstrated 100% growth inhibition of C. gloeosporioides at 2500 ppm . The combi-product fungicides tricyclazole 18% + mancozeb 62%, hexaconazole 4% + zineb 68%, and carbendazim 12% + mancozeb 63% were shown to exhibit 100% growth inhibition at 2000 ppm. Using the dual culture method, five distinct bioagents were tested in vitro for their ability to prevent the development of C. gleosporiodes. Trichoderma harzianum showed up as one of them to be a powerful and effective antagonist of C. gleosporiodes.&#x0D; Conclusion: In vitro fungicide and bioagent testing provides preliminary data on the effectiveness of fungicides against pathogens, guiding field testing. The study evaluated the effectiveness of fungicides and bioagents in suppressing the leaf spot pathogen of mango.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

A sample robust optimal bidding model for a virtual power plant

Nowadays, for controlling crime, surveillance cameras are typically installed in all public places to ensure urban safety and security. However, automating Human Activity Recognition (HAR) using computer vision techniques faces several challenges such as lowlighting, complex spatiotemporal features, clutter backgrounds, and inefficient utilization of surveillance system resources. Existing attempts in HAR designed straightforward networks by analyzing either spatial or motion patterns resulting in limited performance while the dual streams methods are entirely based on Convolutional Neural Networks (CNN) that are inadequate to learning the long-range temporal information for HAR. To overcome the above-mentioned challenges, this paper proposes an optimized dual stream framework for HAR which mainly consists of three steps. First, a shots segmentation module is introduced in the proposed framework to efficiently utilize the surveillance system resources by enhancing the lowlight video stream and then it detects salient video frames that consist of human. This module is trained on our own challenging Lowlight Human Surveillance Dataset (LHSD) which consists of both normal and different levels of lowlighting data to recognize humans in complex uncertain environments. Next, to learn HAR from both contextual and motion information, a dual stream approach is used in the feature extraction. In the first stream, it freezes the learned weights of the backbone Vision Transformer (ViT) B-16 model to select the discriminative contextual information. In the second stream, ViT features are then fused with the intermediate encoder layers of FlowNet2 model for optical flow to extract a robust motion feature vector. Finally, a two stream Parallel Bidirectional Long Short-Term Memory (PBiLSTM) is proposed for sequence learning to capture the global semantics of activities, followed by Dual Stream Multi-Head Attention (DSMHA) with a late fusion strategy to optimize the huge features vector for accurate HAR. To assess the strength of the proposed framework, extensive empirical results are conducted on real-world surveillance scenarios and various benchmark HAR datasets that achieve 78.6285%, 96.0151%, and 98.875% accuracies on HMDB51, UCF101, and YouTube Action, respectively. Our results show that the proposed strategy outperforms State-of-the-Art (SOTA) methods. The proposed framework gives superior performance in HAR, providing accurate and reliable recognition of human activities in surveillance systems.

A Dual Closed-Loop Digital Twin Construction Method for Optimizing the Copper Disc Casting Process

A Universal Accelerated Primal–Dual Method for Convex Optimization Problems

Local convergence of primal–dual interior point methods for nonlinear semidefinite optimization using the Monteiro–Tsuchiya family of search directions

Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

Fruit discoloration and fruit rot disease in longan is a serious disease caused by the pathogenic fungi Pestalotiopsis oxyanthi and Lasiodiplodia pseudotheobromae. This study investigated the potential of antagonistic bacteria as an eco-friendly alternative to traditional fungicides for managing longan diseases caused by these fungal pathogens. Two bacterial isolates, UP-JLS050 and UP-JLS067, demonstrated significant inhibitory activity against the pathogenic fungi P. oxyanthi and L. pseudotheobromae, with high inhibitory activity. The bacterial strains were isolated from leaves and soil in a longan garden, and dual culture methods showed that the isolates UP-JLS050 and UP-JLS067 exhibited the highest inhibitory activity on the fungal pathogens, with percentage inhibitions of 72.67 ± 2.31 and 70.37 ± 1.28 %, respectively. Biochemical methods and molecular techniques, including sequencing of the 16S rDNA gene, were then used to identify the isolates. This confirmed their similarity to Bacillus subtilis and Bacillus amyloliquefaciens with 100 and 99 % identity, respectively. In vitro tests on longan fruit indicated that these antagonistic bacteria, administered at a concentration of 108 CFU/mL, significantly reduced disease incidence by up to 45 % after 9 days of incubation. The incubation of B. subtilis (UP-JLS050) and B. amyloliquefaciens (UP-JLS050) reduced disease incidence by 45 ± 5.77 and 32.5 ± 5.00 %, respectively. HIGHLIGHTS Fruit Discoloration and Rot in Longan: Caused by pathogenic fungi, namely Pestalotiopsis oxyanthi and Lasiodiplodia pseudotheobromae Antagonistic Bacteria for Disease Control: This study explores the potential of antagonistic bacteria as an eco-friendly alternative to traditional fungicides for managing longan diseases caused by these fungal pathogens Effective Inhibition: Two bacterial isolates, UP-JLS050 and UP-JLS067, demonstrated significant inhibitory activity against oxyanthi and L. pseudotheobromae, with inhibitions exceeding 70 % on PDA medium Identification of Bacterial Strains: Molecular techniques, including 16S rDNA gene sequencing, confirmed the identity of these isolates as subtilis and B. amyloliquefaciens Reduced Disease Incidence: In vitro tests on longan fruit indicated that these antagonistic bacteria, administered at a concentration of 108 CFU/ml, significantly reduced disease incidence by up to 45 % after 9 days of incubation GRAPHICAL ABSTRACT

Dual fault warning method for coal mill based on Autoformer WaveBound

Four legume crops, viz., groundnut (Arachis hypogea L.), soybean (Glycine max (L.) Merr.) of Kharif season, chickpea (Cicer arietinum L.), and lentil (Lens esculentum L.) of Rabi season belonging to family Fabaceae were taken for investigation of seed-borne mycoflora. Since these are severely affected by many plant-pathogenic fungi at various stages of the whole life cycle, they were chosen for the study. Many post-harvest fungal pathogens cause severe loss by contaminating the grains with various mycotoxins and also deteriorate the nutritional quality of grains. The goal of any agricultural system is to increase the yield of crops as per the population demand. Various eco-friendly management practices were performed on highly mycotoxin-producing isolates of Aspergillus flavus and A. niger isolated from these grains. Four Trichoderma isolates (T2, T3, T4 and T5) were used as a biocontrol agent in the dual culture method to check antagonism between various Aspergillus isolates and Trichoderma isolates. It gave a satisfactory result in controlling mycelial growth. Achyranthes aspera leaf and stem phytoextract (10%, 20%, 30%, and 40%) was used as a botanical treatment in the well diffusion method, which successfully restricts the Aspergillus spp. mycelial growth.

A finite volume method to solve the Poisson equation with jump conditions and surface charges: Application to electroporation