Incremental Method Research Articles

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Moment evolution equations for rational random dynamical systems: an increment decomposition method

Abstract Statistical moments are commonly used tools for exploring the ensemble behavior in gene regulation and population dynamics, where the rational vector fields are particularly ubiquitous, but how to efficiently derive the corresponding moment evolution equations was not much involved. Traditional derivation methods rely on fractional reduction and Itô formula, but it may become extremely complicated if the vector field is described by multivariate fractional polynomials. To resolve this issue, we present a novel incremental decomposition method, by which the rational vector field is divided into two parts: (proper) fractional polynomials and non-fractional polynomials. For the non-fractional polynomial part, we deduce the variation rate of a statistical moment by the Itô formula, but for the fractional polynomial part we acquire the corresponding variation rate by a relation analogous to that between the moment generating function and the distinct statistical moments. As application of the novel technique, the resultant infinite-dimensional moment systems associated with two typical examples are truncated with the schemes of derivative matching closure and the Gaussian moment closure. By comparing the lower-order statistical moments obtained from the closed moment systems with the counterparts obtained from direct simulation, the correctness of the proposed technique is verified. The present study is significant in facilitating the development of moment dynamics towards more complex systems.

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Convergence analysis of incremental quasi-subgradient method on Riemannian manifolds with lower bounded curvature

We study the convergence analysis of the incremental quasi-subgradient method for solving the sum of geodesic quasi-convex functions on Riemannian manifolds whose sectional curvature is bounded below. The convergence result of the method with diminishing stepsize is established. An application of the studied algorithm to the sum of ratio problems in the setting of Riemannian manifolds with negative sectional curvature is given.

Phase volume correlation approach for overcoming decorrelation in three-dimensional phase-sensitive optical coherence elastography

The dynamic measurement range in phase-sensitive optical coherence elastography (PhS-OCE) is limited for the phase decorrelation induced by pixel-level displacements in precision measurement, where the consideration of the time-resolved incremental method and in-plane pixels tracking method is insufficient to recover the phase holistically. This work presented a phase volume correlation (PVC) approach to handle the phase decorrelation in three-dimensional PhS-OCE. By utilizing the ability of the discontinuous source diagram to quantify voxel phase correlation levels, the PVC establishes a wrapped phase-matching equation aimed at optimizing the number of volumetric source distributions. The three-dimensional pixel-level motions in the deformed phase space can be evaluated by solving the optimization model for phase matching, thereby enabling the reconstruction of the volumetric phase variation corrupted by decorrelation. The large deformations experiments including diffident loadings, i.e., stretching, three-point bending, and light-cured, verified the proposed PPVC approach's of feasibility, reliability, and stability. The contribution of this work can dramatically enhance the dynamic measuring range in three-dimensional PhS-OCE.

Numerical investigation and seismic performance evaluation of an innovative prefabricated structural system

This paper focuses on an innovative structural system with separated gravity and lateral resisting systems (SGLR system), which is suitable for multi-story prefabricated buildings and demonstrates different seismic performance from the traditional rigid frame system (RF system). Elaborate three-dimensional (3D) finite element models for RF and SGLR systems are established and verified by test results. Based on the numerical models, the load-displacement relationship, force mechanism, energy dissipation capacity, local buckling, and stress and strain distribution of SGLR system are investigated in detail in comparison with RF system. In order to conduct the seismic performance evaluation, four groups of RF and SGLR substructures are designed with different lateral stiffness. Pushover analysis and response spectrum analysis are carried out, and safety factors are calculated on the basis of the incremental N2 method. It is indicated that SGLR system has lower loading capacity, smaller safety margin but larger deformation capability than RF system with the same lateral stiffness. Considering that the inter-story drift ratio is a common design index, more stringent design requirements should be proposed for SGLR system to obtain seismic performance equivalent to that of RF system.

A Knowledge Distillation Method Based on Evidence Theory to Prevent Catastrophic Forgetting

Abstract Incremental learning is an emerging machine-learning approach designed to prevent catastrophic forgetting while learning a new task with knowledge retained from previous tasks. However, existing methods often must fully utilize the distributional information of the old task model’s outputs. To alleviate this, we propose a class incremental learning method grounded in evidence theory to leverage the distributional information of outputs from the old task model, which integrates uncertainty estimation into the knowledge distillation process to ensure the new task model effectively learns from the old task model’s distribution information. Specifically, we incorporate uncertainty estimation based on evidence theory to calculate knowledge distillation loss during training. We compute the uncertainty estimates of outputs from both the new and old task models, and then they are used in the knowledge distillation loss calculation. We propose a novel classification strategy that considers outputs’ probability and uncertainty estimates, which could determine the sample category without requiring an additional training phase or supplementary models. Experiments on the CIFAR-100 and ImageNet datasets demonstrate the effectiveness of the proposed method, showing that it outperforms existing methods by improving accuracy by 1%-2%. Results suggest that leveraging the distribution information of model outputs can effectively mitigate catastrophic forgetting in deep learning models within incremental learning scenarios.

DDOWOD: DiffusionDet for open-world object detection

Open-world object detection (OWOD) poses a significant challenge in computer vision, requiring models to detect unknown objects and incrementally learn new categories. To explore this field, we propose the DDOWOD based on the DiffusionDet. It is more likely to cover unknown objects hidden in the background and can reduce the model’s bias towards known class objects during training due to its ability to randomly generate boxes and reconstruct the characteristics of the GT from them. Also, to improve the insufficient quality of pseudo-labels which leads to reduced accuracy in recognizing unknown classes, we use the Segment Anything Model (SAM) as the teacher model in distillation learning to endow DDOWOD with rich visual knowledge. Surprisingly, compared to other existing models, our DDOWOD is more suitable for using SAM as the teacher. Furthermore, we proposed the Stepwise distillation (SD) which is a new incremental learning method specialized for our DDOWOD to avoid catastrophic forgetting during the training. Our approach utilizes all previously trained models from past tasks rather than solely relying on the last one. DDOWOD has achieved excellent performance. U-Recall is 53.2, 51.5, 50.7 in OWOD split and U-AP is 21.9 in IntensiveSet.

An Integrated Framework for Estimating Origins and Destinations of Multimodal Multi-Commodity Import and Export Flows Using Multisource Data

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling.

Transmission and Dissipation of Vibration in a Dynamic Vibration Absorber-Roller System Based on Particle Damping Technology

The research of rolling mill vibration theory has always been a scientific problem in the field of rolling forming, which is very important to the quality of sheet metal and the stable operation of equipment. The essence of rolling mill vibration is the transfer of energy, which is generated from inside and outside. Based on particle damping technology, a dynamic vibration absorber (DVA) is proposed to control the vertical vibration of roll in the rolling process from the point of energy transfer and dissipation. A nonlinear vibration equation for the DVA-roller system is solved by the incremental harmonic balance method. Based on the obtained solutions, the effects of the basic parameters of the DVA on the properties of vibration transmission are investigated by using the power flow method, which provides theoretical guidance for the selection of the basic parameters of the DVA. Furthermore, the influence of the parameters of the particles on the overall dissipation of energy of the particle group is analyzed in a more systematic way, which provides a reference for the selection of the material and diameter and other parameters of the particles in the practical application of the DVA. The effect of particle parameters on roll amplitude inhibition is studied by experiments. The experimental results agree with the theoretical analysis, which proves the correctness of the theoretical analysis and the feasibility of the particle damping absorber. This research proposes a particle damping absorber to absorb and dissipate the energy transfer in rolling process, which provides a new idea for nonlinear dynamic analysis and stability control of rolling mills, and has important guiding significance for practical production.

An incremental permeability detection method for yield strength of ferromagnetic materials based on permanent magnet excitation

ABSTRACT In this paper, in order to meet the engineering requirements of mechanical properties detection of materials with low power consumption, a novel incremental permeability detection system based on permanent magnet excitation is proposed to reduce system power consumption. Initially, a new incremental permeability sensor was designed to extract magnetic incremental permeability (MIP) signal. Through finite element simulation, experimental parameters were optimised, and a rational arrangement of double permanent magnets was devised to furnish an optimally strong AC bias field intensity. Subsequently, a practical detection platform was assembled to extract electromagnetic signal features. From the perspective of domain wall displacement, a characteristic value θ was proposed to predict the yield strength, with experimental results yielding a relative error of less than ±10%. These experiments have validated the capability of permanent magnet-type MIP to enable quantitative detection of the yield strength in ferromagnetic materials.

Nonlinear dynamic study on existing masonry-infilled concrete frames retrofitted with timber panels

This study investigates the dynamic performance of a timber-based seismic retrofit for existing reinforced concrete (RC) buildings, named RC-TP. The core of the proposed retrofit intervention is the replacement of the existing masonry infills with structural timber panels that are connected to the concrete elements using metal dowel-type fasteners and a timber subframe. The unreinforced frames were designed according to codes in force in the 60 s and 70 s, considering exclusively gravity loads to simulate a condition common to a large part of the built heritage across Europe. The seismic performance of single-storey single-bay frames before and after the application of the RC-TP retrofit was assessed via nonlinear simulations, for which the incremental dynamic analysis method was adopted. The results of the extensive dynamic study show the effectiveness of the RC-TP retrofit in increasing the seismic acceleration resisted by the frames and their base shear capacity and in promoting a more ductile failure mechanism. Some general considerations helpful in guiding the implementation of the retrofit system were also drawn.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.

Maximum power point tracking controller for photovoltaic system based on chaos quantum particle swarm optimization – moth-flame optimization hybrid model

To convert photovoltaic arrays to solar energy in a more efficient way, this paper has proposed a maximum power point tracking controller model based on the chaotic quantum particle swarm-mothballing hybrid algorithm. First, the optimization of the particle swarm algorithm is designed to solve defects, such as premature maturity by using the quantum and chaotic strategies. The mothballing algorithm is introduced to help the model find global optimization-seeking more quickly. After that, further optimization was made to operate the tracking model in both offline and real-time parameters. The conductivity increment method and the perturbation observation method were adopted to effectively track the model under different temperatures and light intensities. Finally, the simulation and analysis experiments were carried out on the Simulink platform. The study’s proposed maximum power point tracking controller achieved a steady-state accuracy η2 of 99.84%. In summary, the study has proposed a hybrid intelligent algorithm with extraction of internal parameters. The maximum power point tracker based on the proposed method is proved to be both effective and accurate.

Class incremental learning with analytic learning for hyperspectral image classification

Hyperspectral image classification (HSIC) is crucial for applications in agriculture and environmental monitoring. As ground objects evolve and remote sensing technology progresses, there is a growing need for HSIC models that can adapt to new data classes without requiring to be retrained from scratch. Throughout the continual learning procedure, the model is expected to not only effectively extract spatial–spectral features from the hyperspectral image but also alleviate the issue of catastrophic forgetting, i.e., the model forgets the learned classes’ knowledge when accessing novel classes during the training process. In this paper, for the HSIC task, we propose a class incremental learning method (HSI-CIL) that is based on analytic learning, a technique that converts network training into linear problems. Specifically, The HSI-CIL model consists of a lightweight feature extractor, a Feature Processing Module (FPM) and an Analytic Linear Classifier (ALC). This model does not need data storage for old and new classes and has only one epoch in the incremental learning stage, so it has lower consumption of resources and training time than several attempts have been proposed for addressing catastrophic forgetting. We perform abundant experiments with the proposed HSI-CIL on three publicly available hyperspectral datasets including Indian Pines, Pavia University, and Salinas. The experiments demonstrate that our HSI-CIL exceeds the state-of-the-art class incremental learning (CIL) techniques applied in HSIC with a certain gap.

Awareness-guided incremental control optimization for chilled water system with deep learning model under cold-start scenarios

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Analisis Penambahan Lini Produksi pada PT XYZ pada Proses Pembuatan Alat Steam Sterilizer Menggunakan Metode Incremental Cost Analysis

PT XYZ manufactures medical equipment, including the Steam Sterilizer Autoclave MD 25L. With the increasing demand for healthcare products, particularly the Autoclave MD 25L, production capacity reached its maximum by 2024, causing excessive strain on the workforce. Consequently, alternatives are needed to enhance production capacity without overburdening employees. The decision analysis for expanding the production line was conducted using the incremental cost analysis method, resulting in three alternatives: the existing design, adding one grinding machine and employees, and adding one bending machine and employees. The feasibility analysis confirmed that all three alternatives are viable. Alternative 3 has the highest investment cost, followed by alternative 2, while alternative 1 has the lowest. Initial iterations focused on alternatives 3 and 2, showing a ΔROR of 89.06% with a MARR of 18.36%. Since ΔROR > MARR, alternative 3 was chosen. In the second iteration, comparing alternative 3 with alternative 1 yielded a ΔROR of 77.51%, confirming that alternative 3 remains the best option.

Sample selection of adversarial attacks against traffic signs

In the real world, the correct recognition of traffic signs plays a crucial role in vehicle autonomous driving and traffic monitoring. The research on its adversarial attack can test the security of vehicle autonomous driving system and provide enlightenment for improving the recognition algorithm. However, with the development of transportation infrastructure, new traffic signs may be introduced. The adversarial attack model for traffic signs needs to adapt to the addition of new types. Based on this, class incremental learning for traffic sign adversarial attacks has become an interesting research field. We propose a class incremental learning method for adversarial attacks on traffic signs. First, this method uses Pinpoint Region Probability Estimation Network (PRPEN) to predict the probability of each pixel being attacked in old samples. It helps to identify the high attack probability regions of the samples. Subsequently, based on the size of high probability pixel concentration area, the replay sample set is constructed. Old samples with smaller concentration areas receive higher priority and are prioritized for incremental learning. The experimental results show that compared with other sample selection methods, our method selects more representative samples and can train PRPEN more effectively to generate probability maps, thereby better generating adversarial attacks on traffic signs.

Enhancing the Functionality of Management Internship and Community Service Through Maintenance Of Web Application

Universities play a crucial role in developing high-quality human resources through Internship and Community Service Programs, which prepare students with professional skills and relevant competencies. However, implementing these programs often faces issues such as inefficiencies in registration, documentation, and progress reporting. To address these challenges, a university department developed and implemented a comprehensive web-based application system. This research aims to improve and refine the application through software maintenance processes, employing an iterative incremental method with phases including planning, requirements, analysis and design, implementation, testing, and evaluation, conducted repeatedly according to the prioritized order of feature improvements. After being maintained, the application is tested through functional testing using black box techniques and automation tools. Results indicate that the maintenance process successfully ensured the application functions according to established scenarios and requirements, thereby enhancing support for administrative processes of internships and community service programs and overcomes previous management challenges.

Experiences from launching the bridge over Gottleuba‐valley (Gottleubatalbrücke)

AbstractThis article briefly describes the project background, timelines, structural data, actually achieved milestones and current stage of project of the bridge over Gottleubavalley in Pirna, Saxony, Germany. The bridge is being built using incremental launching method, the main challenges of the launching process are briefly described together with some learning points.The winning design from the architectural competition in 2006 proposed very slender (1 to 35 !) hybrid steel‐concrete semi‐integral frame crossing the 70m deep and 1000m wide valley with nine spans (92 – 116 – 120 – 120 – 124 – 108 – 92 – 76 m) with maximum span length of 124m. The paper describes the challenges the team faced during the preparation and launching of a very slender bridge deck with deflections up to 6m when touching down at the bridge pier. By the time of the venue, the incremental launching will be already finished, and the lowering works will be in progress.