Counting Algorithm Research Articles

A Reactive Power Injection Algorithm for Improving the Microgrid Operational Reliability

Stand-alone microgrids have become reliable and efficient solutions for remote areas and critical infrastructures. However, the converters within these microgrids experience long-term complex power fluctuations caused by random variations in micro sources and loads. These power fluctuations induce thermal cycling in semiconductor chips, leading to thermal fatigue failure and compromising the safety and reliability of both the converter and microgrid operation. To address this issue, this paper proposes a reactive power injection algorithm aimed at reducing the output power fluctuation of the converter. The algorithm implements reactive power injection at the converter control level, thereby restructuring the output power profile and resulting in reduced junction temperature fluctuations in IGBTs. This approach effectively mitigates thermal stress within the material layers of the module, extending the lifetime of power devices and improving the operational reliability of the microgrid. The algorithm implementation is based on the PQ control strategy, integrating the power triangle with the envelope detection technique. Furthermore, the lifetime prediction process utilizes the electro-thermal coupling model, the rainflow counting algorithm, and the Lesit model. Simulation results demonstrate that, for an active power fluctuation range of 10 kW to 80 kW and an equivalent RC time constant of 22.5 s, the algorithm achieves a significant reduction of 62.64% in the amplitude of output power fluctuation and extends the lifetime of power devices by 74.13%. The obtained data showcase the effectiveness of the algorithm in enhancing the lifetime of power devices and further improving the microgrid operational reliability under specific parameter conditions.

Edge-based wireless imaging system for continuous monitoring of insect pests in a remote outdoor mango orchard

Mango production is a prominent tropical fruit industry worldwide. However, outdoor mango cultivation is susceptible to crop damage caused by insect pests and harsh environmental conditions. Integrated pest management (IPM) has emerged as a proposed solution to this problem. IPM utilizes data-driven and environmentally-friendly methods to suppress insect pest populations. Nevertheless, the collection of insect pest population data remains a laborious process, necessitating automation. This paper presents an image-based monitoring system to automatically record insect pest populations and environmental conditions in mango orchards. The system comprises solar-powered sensor nodes capable of periodically acquiring and analyzing sticky paper trap images. A modular deep learning-based algorithm was developed to detect and classify insect pests into seven classes, including major insect pests of mango such as thrips, mango leafhopper, and oriental fruit fly, with an average classification F1-score of 0.96. Unlike other insect counting algorithms, the algorithm reliably classifies insect pests according to different taxonomic levels even in non-laboratory environments. The monitoring system was tested and deployed in a remote mango orchard for over two years. The collected spatiotemporal information was analyzed to demonstrate the benefits of using the proposed system and recommend new IPM strategies. Temporal data analysis revealed a significant decrease in the count of selected insect pests after using the system, enabling identification of insect hotspots through statistical methods. This work presents a breakthrough in hardware and software solutions for developing smarter insect pest monitoring systems, leading to better IPM strategies.

AN EXACT SOLUTION FOR REAL-LIFE TRANSSHIPMENT PATH PROBLEM

In industrial engineering, transportation planning, vehicle routing problem, warehousing, inventory management, and customer service are logistics problems. Graph theory algorithms provide solutions to logistics problems such as the shortest path, minimum spanning tree, and vehicle routing problems. In a logistics company system with branches and transfer centers to which the branches are affiliated, if the sorting process is carried out in the transfer centers, the deliveries collected from the branches must be transported to a transfer center. Thus, there are situations where delivery is transferred in the sending branch, the sending transfer center, the receiving transfer center, and the receiving branch, respectively. In this flow, transferring with a single transfer center without visiting two transfer centers reduces the total cost. While moving from the sender transfer center to the receiver transfer center, stopping by some branches on the way allows us to complete the transfer process with a single transfer center and eliminates the necessity of leaving the vehicle from the receiver transfer center to these branches again. Thus, the number of vehicles that need to go from the receiver transfer center to the branches is reduced. The mentioned logistics structure is defined as a graph that is considered a network design problem. Given the sender transfer center S, the receiver transfer center T, the set of branches A connected to S, and the set of branches C that are not connected to S or T, a counting algorithm that gives the minimum value route among all combinations are designed in order to find the optimal route from the source node s ∈ A∪{S}, to the target node t=T. The algorithm has been implemented in Python and Gams and tested by the different number of elements of the set A and the set C.

Analysing the effect of defects on stress concentration and fatigue life of L-PBF AlSi10Mg alloy using finite element modelling

Additive manufacturing (AM) is a developing manufacturing technology, which provides excellent attributes compared to other manufacturing techniques. However, one of the critical challenges is the presence of defects that hinder the mechanical properties of the parts, particularly the fatigue life. Experimental understanding of fatigue is a cumbersome process. Therefore, numerical prediction based on specified conditions (such as porosity and applied load) can be an alternative to experimental analysis at the design stage of AM parts. In this study, elastic–plastic finite element analysis (FEA) is performed to obtain the stress distribution around pores, and their resultant effect on fatigue life for L-PBF (laser powder bed fusion) produced AlSi10Mg alloy samples. The stress field is calculated for both single and multiple pore models, where stress concentration is evaluated as a function of the pore’s location and its size. It is found that both pore location and size affect the stress field; however, location effects dominate over pore size. For the same remote applied stress level, the stress concentration around the pore increases with an increase in pore size, and the local maximum stress occurs near the pores that are closest to the surface. The current study also evaluates the relative effect of porosity fraction, average pore size, and location. It is found that the magnitude and sensitivity of stress concentration are hierarchically controlled by porosity location, pore size, and porosity density. A multi-scale finite element (FE) model is proposed based on inherent porosity data measured using Computed Tomography (CT) to predict overall fatigue life. The fatigue cycles are calculated using the rainflow counting algorithm in FE Safe using the stress–strain data obtained from the proposed FEA model. Using the proposed model, it is possible to generate S–N curves for any loading condition for a given porosity condition (porosity density and average pore size). The S–N curve results obtained from the FE model are compared to the experimental observations. The predicted fatigue life shows approximately 5% error with experimental results at high stress loading conditions. However, the proposed model overpredicts the fatigue life at low stress loading by almost 30%.

Deep learning in crowd counting: A survey

AbstractCounting high‐density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three‐tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small‐scale, large‐scale and hyper‐scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data‐driven perspective: multi‐scale networks, single‐column networks, multi‐column networks, multi‐task networks, attention networks and weak‐supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three‐tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small‐scale datasets has slowed down. There are few new datasets and algorithms on small‐scale datasets. The studies focused on large or hyper‐scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real‐world scenarios and processing large crowds in real‐time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.

Digital twin-based structural health monitoring by combining measurement and computational data: An aircraft wing example

Digital twin is a concept that utilizes digital technologies to mirror the real-time states of physical assets and extract the hidden yet valuable information of physical assets for optimization, decision-making or scheduling. By combining measurement and computational data, this paper presents a digital twin-based structural health monitoring framework of physical assets. The process for building the measurement-computation combined digital twin (MCC-DT) involves four steps. First, an artificial intelligence-driven load identification method combining measurement and computational data is employed to recognize the loads applied on physical assets. Two approaches were proposed to realize load identification, based on single fidelity surrogate models and deep learning techniques, respectively. Second, multi-fidelity surrogate (MFS) models are applied to improve the accuracy in the MCC-DT. Two routes for implementing the MFS models are introduced and the advantages and shortcomings of both are analyzed. Third, an online rainflow counting algorithm is developed to calculate the degradation of the physical assets. The main advantage of the algorithm is that it can provide a near real-time estimation for the damage accumulated of physical assets. Finally, the data generated from the first three steps can be fused into a three-dimensional scene using Web graphics library to provide an intuitive view of the MCC-DT. To describe the implementation details of the framework and verify its applicability and effectiveness, the MCC-DT was established using an aircraft model as an example.

Design Method of Full-scale Fatigue Test Load for Wind Turbine Blade

The blade is one of the key components of a wind turbine, and the alternating fatigue load is the main factor leading to the fatigue failure of wind turbine blades. The accuracy of load determines the accuracy of the wind turbine blade test. Based on the equivalent torque time history data from the wind load acting on the blade root, the load of the two-dimensional matrix is obtained by using data compression technology and a rain flow counting algorithm. According to the capture matrix of load data, the frequency of various design loading cases and the distribution of wind speed, the symmetrical load spectrum is calculated and compiled. The fatigue load of a wind turbine blade is calculated for a certain type of 2MW blade, which lays a foundation for the design and test of a wind turbine blade fatigue loading system.

The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults.

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83-0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53-0.68 and 0.55, 95% CI 0.46-0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32-0.74). We have validated a digital susceptibility/risk biomarker-pfSTEP-that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm.

COP: A Combinational Optimization Power Budgeting Method for Manycore Systems in Dark Silicon

Dark silicon is a phenomenon of under-utilization in today's manycore systems due to power and thermal limitations. In order to improve the performance of dark silicon systems, it is necessary to adopt dynamic power constraints for different core mapping decisions. However, existing power budgeting methods are generally over pessimistic, e.g. Thermal Safe Power (TSP), or over optimistic, e.g. Greedy based Dynamic Power (GDP). This paper proposes a practical power budgeting method, called Combinational Optimization Power (COP). Different from existing methods, which ignore some actual factors, such as communication overhead and lifetime reliability, COP formulates the power budgeting problem as a thermal-constrained combinational optimization power problem. For the steady-state case, COP achieves the target fusion of optimized temperature and communication energy consumption by applying task priority ranking and task-to-core mapping. For the transient case, COP uses the rainflow counting algorithm to construct the reliability framework based on the thermal cycling failure mechanism, and then establishes a linear time-invariant transient temperature model to obtain the core mapping selection and the corresponding dynamic power budget. Experimental results demonstrate that COP is capable of providing an optimized core mapping decision, which can maximize power budget while ensuring the system performance.

Image-Based Insect Counting Embedded in E-Traps That Learn without Manual Image Annotation and Self-Dispose Captured Insects

This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm in that (a) it does not require manual annotation of images to learn how to count targeted pests, and (b) it self-disposes the captured insects, and therefore is suitable for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests inside a pheromone-based funnel trap. The device also receives commands from the server, which configures its operation, while an embedded servomotor can automatically rotate the detached bottom of the bucket to dispose of dehydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the autonomous operational capability. We study cases that are underrepresented in the literature such as counting in situations of congestion and significant debris using crowd counting algorithms encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep learning approaches (YOLOv7/8, crowd counting, deep learning regression). Interestingly, there is no one optimal clear-cut counting approach that can cover all situations involving small and large insects with overlap. By weighting the pros and cons we suggest that YOLOv7/8 provides the best embedded solution in general. We open-source the code and a large database of Lepidopteran plant pests.

Hierarchical clustering algorithm for dendrogram construction and cluster counting

Hierarchical clustering algorithm for dendrogram construction and cluster counting

Fatigue Life Convergence of Offshore Wind Turbine Support Structure According to Wind Measurement Period

This paper investigated the fatigue life of offshore wind turbine (OWT) support structures. For this purpose, a 3 MW-capacity typical wind turbine is investigated using time-domain finite element simulations. In numerical simulations, different stochastic wind models corresponding to different accumulation periods are applied. Then, the stress-based fatigue life is estimated following the rain-flow counting algorithm and Palmgren-Miner linearly cumulative damage rule. The study also addresses the joint distribution of loads at the site of interest. Generally, the study emphasizes the significance of the long-term distribution of the applied environment loads and its influence on the fatigue life of OWT’s substructures. The results imply that the wind measurement period is directly linked to the fatigue life of offshore wind turbine support structures. Accordingly, its fatigue life is significantly reduced at the 25-year accumulative period of wind. Therefore, this study recommends that a sufficient number of accumulative periods of wind and other environmental loads should be considered appropriately.

A Survey of Crowd Counting Algorithm Based on Domain Adaptation

Crowd counting, the task of estimating the number of individuals in a crowded scene, has gained increasing attention in computer vision research. However, crowd counting remains a challenging problem due to the complex and diverse nature of crowd scenes. In recent years, domain adaptation has emerged as a promising approach to improve crowd counting performance by adapting a pre-trained model to a target domain with different characteristics. This paper provides a survey of domain adaptation-based crowd counting algorithms, including their methods, datasets, and evaluation metrics. Overall, domain adaptation shows great potential in improving the accuracy and robustness of crowd counting algorithms, and further research in this direction is expected to lead to more effective and practical crowd counting solutions.

DJ-1 Oncogene as a Potential Diagnostic and Prognostic Biomarker for Head and Neck Cancer.

Background Current methods used to diagnose and prognosticate oropharyngeal cancer have contributed to unfavorable patientsurvival rates that have not significantly improved for the last several decades. Precision medicine oncology relies on molecular diagnostics and biomarkers to supplement existing methods of detecting and prognosticating cancers. This study evaluated the expression of DJ-1, an oncogene that is implicated in the pathogenesis of oral squamous cell carcinoma (OSCC), the most common type of head and neck cancer, to determine its utility as a diagnostic and prognostic biomarker. Methodology Immunohistochemistry (IHC) was performed on 13 normal oral mucosa tissue samples and 143 OSCC tissue samples of varying histopathological grades. Computer-assisted image analysis was performed using the Aperio ImageScope software from Leica Biosystems (Buffalo Grove, IL), which utilizes an algorithm of positive pixel counting for the quantification of immunoreactivity and the percentage of positive cell staining, generating a histo-score (H-score). The comparisons of the average H-scores of the different groups were made using a two-tailed T-test with P ≤ 0.05 set as the level of significance. Results The study found a significant increase in DJ-1 expression in oral squamous cell carcinoma tissue samples in comparison to the normal oral mucosa tissue samples. Additionally, the study documented a significant upregulation in DJ-1 expression in the OSCC tissue samples with high histopathological grades compared to the OSCC tissue samples with low histopathological grades. Conclusions DJ-1 expression patterns were able to reliably differentiate between oral squamous cell carcinoma and the normal counterpart tissues of the oral mucosa, thereby highlighting its role as a potential diagnostic biomarker. Moreover, DJ-1 expression significantly correlates with the OSCC histological grade, which serves as an indicator of the differentiation status and a predictor of the biological behavior of malignant neoplasms, adding to DJ-1's potential utility as a prognostic biomarker for this common type of head and neck cancer.

Dynamic identification and automatic counting of the number of passing fish species based on the improved DeepSORT algorithm

In this paper, based on the improved DeepSORT algorithm, four target species of passing fish (Schizothorax o’connori, Schizothorax waltoni, Oxygymnocypris stewartii and Schizopygopsis younghusbandi) from a fishway project in the middle reaches of the Y River were used to achieve dynamic identification and automatic counting of passing fish species using fishways monitoring video. This method used the YOLOv5 model as the target detection model. In view of the large deformation by fish body twisting, the network structure of the re-identification (ReID) model was deepened to strengthen the feature extraction ability of the model. It was proposed to identify and track fish that cross the line by setting a virtual baseline to achieve the dynamic identification of fish species passing and the automatic counting of upward and downward quantities. The results showed that 1) among the five models, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the highest value of mean average precision (mAP) was 92.8% achieved by the YOLOv5x model. Specifically, recognition accuracies of 96.95%, 94.95%, 88.79%, and 91.93% were recorded for Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii, respectively. 2) The error rate of the improved ReID model was 20.3%, which was 20% lower than that before the improvement, making it easier for the model to obtain target features. 3) The average accuracy of the improved DeepSORT algorithm for counting four target fishes was 75.5%, among which the accuracy of Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii were 83.6%, 71.1%, 68.1%, and 79.3%, respectively. Meanwhile, the running speed was 44.6 fps, which met the real-time monitoring. This method is the first to implement intelligent identification of the target passing fish in fishways projects, which can accumulate long series monitoring data for fishways operation and management and provide a technical solution and reference for the work related to the realization of intelligent and informative passing fish monitoring.

A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

Battery-hydrogen vs. flywheel-battery hybrid storage systems for renewable energy integration in mini-grid: A techno-economic comparison

Decentralized renewables power production is rapidly growing because of environmental concerns. With the purpose of maximizing renewable exploitation, energy storage systems integration in Mini-Grids (MGs) is needed. Hybrid energy storage systems (HESSs) are promising to obtain enhanced performances in terms of both capacity and responsiveness, yet their feasibility may be hindered by design and management choices impacting the economic competitiveness. In this paper, two HESSs are analysed and compared in a real case-study, namely reversible solid oxide cell (rSOC)/Li-ion battery and flywheel/Li-ion battery systems. The analysis here developed handles annual photovoltaic generation and electric load actual dataset and includes: i) a statistical analysis to obtain the most representative days, ii) dynamic modeling of the HESSs, iii) simulations to define components sizing and to extrapolate the evolution of annual battery state-of-charge, iv) battery lifespan assessment through application of the rainflow cycle counting algorithm, and v) a detailed economic evaluation (Levelized Cost of energy LCOE, Levelized cost of Storage LCOS and their sensitivity to electricity market price) including subsidies for self-consumption. For both HESSs, performance indexes of reduction of energy withdrawal from the grid and self-consumption increase are fixed at about 30 % and 70 % respectively, with reference to the features of the investigated MG. As main outcomes for this integrated methodology, it appears that self-consumption rewards (i.e.110 €/MWh) let the flywheel/Li-ion battery HESS reach a LCOS agreeing with 2021 average market parity. On the contrary, LCOS is more than two-fold for the rSOC/Li-ion battery HESS, being equal the impact on the MG management and its energy independence from the main power grid. The integrated methodology is adequate for critically reviewing design and management choices and understanding the impact of the market on the profitability of a sub-system, with insight on energy storage units.

Zliczanie szybkich obiektów z wykorzystaniem kamery zdarzeniowej

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These cameras transmit, in an asynchronous manner, information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, and correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of falling grains to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed.

High-Performance Computation of the Number of Nested RNA Structures with 3D Parallel Tiled Code

Many current bioinformatics algorithms have been implemented in parallel programming code. Some of them have already reached the limits imposed by Amdahl’s law, but many can still be improved. In our paper, we present an approach allowing us to generate a high-performance code for calculating the number of RNA pairs. The approach allows us to generate parallel tiled code of the maximal dimension of tiles, which for the discussed algorithm is 3D. Experiments carried out by us on two modern multi-core computers, an Intel(R) Xeon(R) Gold 6326 (2.90 GHz, 2 physical units, 32 cores, 64 threads, 24 MB Cache) and Intel(R) i7(11700KF (3.6 GHz, 8 cores, 16 threads, 16 MB Cache), demonstrate a significant increase in performance and scalability of the generated parallel tiled code. For the Intel(R) Xeon(R) Gold 6326 and Intel(R) i7, target code speedup increases linearly with an increase in the number of threads. An approach presented in the paper to generate target code can be used by programmers to generate target parallel tiled code for other bioinformatics codes whose dependence patterns are similar to those of the code implementing the counting algorithm.

Distributed Approaches to Butterfly Analysis on Large Dynamic Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2,2)-biclique). It has been a popular research topic with applications in document clustering, spam group detection, and analysis of affiliation networks. In such applications, the graphs are not only large, but they evolve quickly with new edges being continuously added/deleted. While existing centralized techniques could solve the tip decomposition problem for static bipartite graphs, they are not efficient for maintaining the tip numbers of vertices on large-scale graphs. In this paper, we study butterfly analysis problems on bipartite graphs in a distributed environment with the vertex-centric model. We first extend a centralized butterfly counting algorithm to a distributed version, called DBCA. An ingenious message aggregation strategy is designed to reduce massive redundant messaging and avoid the memory overflow problem while processing large-scale graphs. Based on the results of DBCA, we develop a distributed tip decomposition algorithm (DTDA) to get the tip number of each vertex in parallel. Finally, to maintain the tip numbers of vertices efficiently while graphs evolve over time, we explore a distributed tip maintenance algorithm (DTMA) along with a novel task-split strategy. Specifically, for an updated edge (insertion/deletion), several sub-tasks will be generated in line with the topology structure of the original bipartite graph. To our best knowledge, this is the first study to process the butterfly analysis problems in a distributed environment. In addition, comprehensive experiments have been conducted on real-world bipartite graphs. The experiment results demonstrate that our proposed algorithms are efficient and scalable.