Iterative Matching Algorithm Research Articles

A rolling horizon planning approach for short-term demand supply matching

Demand fulfillment and order management are important functions in semiconductor supply chains to interact with customers. In this paper, an iterative short-term demand supply matching (STDSM) algorithm based on mixed-integer linear programming (MILP) is proposed. This approach repromises orders taking into account the finite capacity of the shop floor. Decomposition is used to obtain computationally tractable subproblems. The STDSM approach is applied together with master planning and allocation planning in a rolling horizon setting. A simulation model of a simplified semiconductor supply chain is used for the rolling horizon experiments. The experiments demonstrate that the proposed STDSM scheme outperforms conventional business rule-based heuristics with respect to several delivery performance-related measures and with respect to stability.

PMP: A partition-match parallel mechanism for DNN inference acceleration in cloud–edge collaborative environments

To address the challenges of delay-sensitive deep learning tasks, Deep Neural Network (DNN) models are often partitioned and deployed to the cloud–edge environment for parallel and collaborative inference. However, existing parallel coordination mechanisms are not suitable for the cloud–edge environment, as the high inter-layer dependence of DNNs can increase transmission latency and wait times for inference, which contradicts the advantage of low latency in edge computing. To resolve this contradiction, the PMP mechanism takes into account the inter-layer transfer dependence of partitioning solutions and employs a multi-objective equalization algorithm to derive DNN model partitioning strategies suitable for multi-way parallel computing. Moreover, the mechanism establishes a DNN inference time prediction model based on these partitions and utilizes an iterative matching algorithm to approximate an optimal DNN inference workflow. Extensive evaluations of the proposed mechanism are conducted using various DNN models, and the results demonstrate its superiority over existing schemes, including local, CoEdge, and EdgeFlow. Notably, PMP achieves significant reductions in total inference latency compared to these schemes, with reductions of 80.9%, 37.9%, and 9.1%, respectively.

An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence

The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms.

KEGG_Extractor: An Effective Extraction Tool for KEGG Orthologs.

The KEGG Orthology (KO) database is a widely used molecular function reference database which can be used to conduct functional annotation of most microorganisms. At present, there are many KEGG tools based on the KO entries for annotating functional orthologs. However, determining how to efficiently extract and sort the annotation results of KEGG still hinders the subsequent genome analysis. There is a lack of effective measures used to quickly extract and classify the gene sequences and species information of the KEGG annotations. Here, we present a supporting tool: KEGG_Extractor for species-specific genes extraction and classification, which can output the results through an iterative keyword matching algorithm. It can not only extract and classify the amino acid sequences, but also the nucleotide sequences, and it has proved to be fast and efficient for microbial analysis. Analysis of the ancient Wood Ljungdahl (WL) pathway through the KEGG_Extractor reveals that ~226 archaeal strains contained the WL pathway-related genes. Most of them were Methanococcus maripaludis, Methanosarcina mazei and members of the Methanobacterium, Thermococcus and Methanosarcina genus. Using the KEGG_Extractor, the ARWL database was constructed, which had a high accuracy and complement. This tool helps to link genes with the KEGG pathway and promote the reconstruction of molecular networks. Availability and implementation: KEGG_Extractor is freely available from the GitHub.

Iterative Fine Point-Set Matching of a Free-Form Surface Based on the Point-to-Sphere Distance

Three-dimensional point-set matching that aligns the moving point set with the fixed design model is an increasingly popular technique in manufacturing automation, which also increases the demand for matching performance. In this article, an iterative fine matching algorithm based on point-to-sphere distance is proposed to balance convergence accuracy, stability, and speed. By considering the neighborhood of a fixed point as a spherical surface, a point-to-sphere distance from a moving point to the design model is derived to improve the distance calculation accuracy with respect to unknown motion parameters. A point-to-sphere iterative closest point (ICP) matching method is presented, which translates the matching into a nonlinear optimization problem. Using the Newton method, a second-order Taylor expansion is applied to iteratively solve the motion parameters. Theoretical analysis verifies the advantages in convergence performance. It is proven that the proposed method can show faster convergence speed than point-to-point ICP and better convergence stability than point-to-plane ICP in large initial poses. Experiments implementing five models verify the feasibility of the point-to-sphere distance and the corresponding matching method.

Improved Particle Swarm Optimization Screening Iterative Algorithm in Gravity Matching Navigation

The promotion of matching precision is one of the key technical problems of gravity-assisted inertial navigation. In this study, first, the viability of the particle swarm optimization (PSO) algorithm applied to gravity matching inertial navigation is analyzed, and the original PSO algorithm is improved. The similarity of single-point matching is used to round off the matching result points, and the difference between the matching result coordinates and the corresponding inertial coordinates is calculated over a certain period to filter out the relatively poorer matching points. Finally, by calculating the coordinate distance of the first and last points between the consecutive valid matching segments and setting the limit difference, the coordinates of the invalid points between two adjacent valid matching points that meet the limit difference are projected to realize the correction of matching points. The test results show that the improved PSO-based screening iterative gravity matching algorithm can avoid the additional matching error caused by the setting of the search resolution compared with the PSO, new self-organizing hierarchical PSO (NHPSO), and adaptive PSO (APSO) matching algorithm and has significant improvement in the matching accuracy.

Radio Resource Allocation for Integrated Sensing, Communication, and Computation Networks

Integrated sensing, communication, and computation (ISCC) will become a key enabler for automation applications. However, since the performance region of ISCC has a higher dimension than those of traditional wireless networks, existing schedulers typically fail to simultaneously meet the heterogeneous requests in ISCC. In this work, we propose a novel wireless scheduling architecture to explore the coordination gains of sensing, communication, and computation from a perspective of joint optimization. Specifically, we first construct an implementation framework of ISCC by combining the mobile edge computing paradigm with the integrated sensing and communication technology, where the inherent tradeoff between sensing, communication, and computation performance is characterized. Next, a joint device association and subchannel assignment problem is formulated to capture the network externalities induced by resource competition among mobile devices with multi-functional requirements. Due to its intractability, we then reformulate it in the matching theoretical manner. To obtain a mutually satisfactory solution under externalities, an iterative matching algorithm is developed by introducing pairwise stability and proved to be convergent and stable. The extensive simulations elucidate the significant superiority of our proposed scheme over those externality-unaware wireless schedulers.

A population-based descriptive study of neonatal abstinence syndrome using hospital discharge and birth certificate data

ABSTRACT Background Neonatal abstinence syndrome (NAS), largely a consequence of prenatal opioid exposure, results in substantial morbidity. Population-based studies of NAS going beyond Medicaid populations and hospital discharge data (HDD) alone are limited. Using statewide Tennessee (TN) HDD and birth certificate (BC) data, we examined trends and evaluated maternal and infant factors associated with NAS. Methods We conducted a population-based descriptive study during 2013–2017 in TN. NAS infants were identified with International Classification of Diseases (ICD)-9-Clinical Modification (CM) and ICD-10-CM codes in HDD and linked to BC data using iterative deterministic matching algorithms. Descriptive analyses were conducted for infant and maternal factors (exposures) by NAS (outcome). Multivariable logistic regression models were used to estimate adjusted ORs and 95% CIs. Results NAS incidence increased from 13.4 to 15.4 per 1,000 live births between 2013 and 2017 (15% increase; ptrend <0.001), but remained stable in 2017. In adjusted models, maternal factors associated with reduced odds of NAS included breastfeeding (OR:0.55, 95%CI:0.52–0.59) and prenatal care (OR:0.36, 95%CI:0.32–0.41). Smoking, preterm birth, and lower birthweight were associated with increased odds of NAS. Conclusions This study highlights the value of utilizing surveillance data to monitor trends and correlates of NAS to inform prevention efforts and targeting of public health resources.

Environment scenario identification based on GNSS recordings for agricultural tractors

• Defined five scenarios: open-sky, low-density trees, high-density trees, high wall/viaduct and tunnel/depot. • Environment scenario model built using a four-state Markov chain model. • A two-stage iterative matching algorithm for road segmentation and scenario identification. • Environment scenarios identification with an overall accuracy of 97.08%. A Global Navigation Satellite System (GNSS) is an essential part of the navigation system of autonomous tractor, which performs well in an open field but it might be affected by the local surroundings (e.g., tree canopies, buildings and overpasses) when traveling along a road. A reliable and cost-effective navigation system is important to ensuring that autonomous tractors operate reliably and safely with high precision positioning in a complex environments. The system requires an adaptive navigation strategy based on environment scenarios, and the identification of the environment scenario is a prerequisite. The present paper proposes an environment scenario identification model, laying a foundation for designing a scenario-based navigation strategy. First, the complex road surrounding environments are classified into five scenarios of open sky, low-density trees, high-density trees, high wall/overpass and tunnel/depot according to the GNSS positioning performance. Second, the environment is reconstructed using a four-state Markov chain model, and a two-stage iterative matching algorithm is designed to segment the recorded GNSS data and identify the scenario of each segment. The performance of the model is then evaluated using GNSS data sets recorded along the route from a depot to a field in Miyun, Beijing. The scenario identification results show that the developed model can identify all defined scenarios, with an overall accuracy of 97.08%. The identification accuracies for the scenarios of open sky, low-density trees, high-density trees, high wall/overpass and tunnel/depot are 97.32%, 96.30%, 95.35%, 94.29% and 100.00%, respectively.

Parallel Matching Pursuit Algorithm to Decomposition of Heart Sound Using Gabor Dictionaries

In order to improve the performance of matching pursuit algorithm, we propose a Parallel Matching Pursuit algorithm to decompose phonocardiogram sounds of long duration. The main goal is to demonstrate the performance of the Parallel Matching Pursuit algorithm (PMP) compared with traditional iterative matching pursuit algorithm to decompose normal and pathological heart sounds of Phonocardiogram (PCG) using a Gabor dictionary. This architecture is implemented in open source Java SE 8 using a concurrency library, which is able to reduce computational cost using multi-threading until 83 % compared with traditional Matching Pursuit. Java language is widely used in the creation of web components and enterprise solutions so based on this point the main idea of this research is to set the base to implement Parallel Matching Pursuit algorithm (PMP) on web platforms focused on the monitoring of heart to sounds. This implementation allows exploring and applying iterative algorithms or sparse approximation which require processing long audio signals with low processing time.

Part-to-full shape matching of different human subjects

Shape matching is a fundamental operation in digital geometry processing and computer graphics. Challenges in shape matching include finding correspondences of partial shapes with deformations, as well as topological noise and ambiguities. This paper presents a partial shape correspondence algorithm based on the concept of the functional map. An iterative dense matching algorithm, incorporating sparse and guided dense matching, is proposed along with a new objective function including both descriptor matching error and transformation error. Rank estimation with the rank direction is proposed to achieve more accurate slope approximation of the functional map. The slope is beneficial because it directly influences the matching efficiency. The experimental results obtained using FAUST and SHREC′16 datasets demonstrate the effectiveness of our proposed algorithm for matching the shapes of different human subjects and shapes with large missing parts compared with state-of-the art algorithms. The proposed algorithm provides an average geodesic distance of <0.033 even when the missing part is up to 80% of the area.

Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

Computation Offloading in Hierarchical Multi-Access Edge Computing Based on Contract Theory and Bayesian Matching Game

Multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs’ CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity.

Channel Estimation Performance Analysis of FBMC/OQAM Systems with Bayesian Approach for 5G-Enabled IoT Applications

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level

Comparing outcomes across different levels of trauma centers is vital in evaluating regionalized trauma care. With observational data, it is critical to adjust for patient characteristics to render valid causal comparisons. Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. We implement the evidence factors method for binary outcomes, which includes a randomization-based testing strategy and a sensitivity analysis for hidden bias in three-group matched designs. We apply our method to the Nationwide Emergency Department Sample data to compare emergency department mortality among non-trauma, level I, and level II trauma centers. Our tests suggest that the admission to a trauma center has a beneficial effect on mortality, assuming no unmeasured confounding. A sensitivity analysis for hidden bias shows that unmeasured confounders, moderately associated with the type of care received, may change the result qualitatively. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Passive vision measurement for robust reconstruction of molten pool in wire and arc additive manufacturing

Robust measurement of molten pool in wire and arc additive manufacturing can help better understand the geometrical size of fabricated parts and even offer feedback to control this process. Most of existing detection methods are not sufficient to express full geometric features of molten pool. In this paper, a novel passive vision sensing system using a single camera is designed based on the refraction principle of a biprism. Intrinsic and external parameters of left-right virtual cameras and their relative position are solved through a planar pattern, and an image pair is rectified by transmission projection. A global-based iterative matching algorithm is optimized for calculating the left-right parallax images of the image pair, and coordinates of space points are calculated via the triangle measuring method. The proposed sensing system and algorithms are validated by a standard cylinder. The results show that the width error of the calculated cylinder appearance is approximately 3.02% and the maximum height error is 4.15%. Finally, molten pool geometry in gas metal arc (GMA) additive manufacturing is restructured, proving the effectiveness of the virtual binocular vision system and the accuracy of the reconstruction algorithms.

REFORM: Rotor Estimation From Object Resampling and Matching

In this paper we tackle the problem of correspondence and rotor estimation between models composed of geometric primitives of different types. We frame this problem as searching for the rotor that takes a query model to a reference model. The situations that we consider are those in which our query model: contains additional primitives not present in the reference; is missing primitives that are present in the reference. We will also look at cases in which there are a large number of primitives per model. These are all common issues facing any SLAM-type (simultaneous localisation and mapping) systems. To overcome these problems we introduce an inter-object rotor magnitude-based matching function and a subsampled iterative rotor estimation and matching algorithm. We title the finished algorithm: Rotor Estimation From Object Resampling and Matching—REFORM. REFORM builds on ideas from the RANSAC (RAndom SAmple Consensus) [7] and ICP (Iterative Closest Point) [3, 11] algorithms and extends these to multivector correspondence. It is easily parallelisable and designed for good convergence performance with models of real objects.

Scheduling in Multi-Wavelength Ring-Based Optical Networks-on-Chip

Synchronous networks-on-chip (NoC) require tailored schedulers achieving low latency, high throughput, and fairness while avoiding packet collisions. Efficient schedulers exist for rearrangeable non-blocking NoC. However, NoCs fabricated in integrated photonics typically suffer a blocking behavior due to the limitations of the switching capabilities in the space and wavelength domains. This paper discusses a scheduler for an integrated optical NoC based on a ring topology and realized with multiple resonating microrings (multi-microring, MMR). Scheduling in MMR architecture consists of the conventional matching sub-problem and the wavelength assignment sub-problem. The paper’s contribution is twofold. An iterative parallel wavelength matching (iPWM) algorithm is presented for jointly addressing both sub-problems. iPWM achieves performance similar to a two-step scheduler based on sequential iSLIP and first-fit wavelength assignment, but with a computational complexity lower than and independent of the number of wavelengths. A comprehensive comparison with the two-step scheduler based on the more complex maximum weighted matching algorithm and with an optimal scheduler is also carried out for the case of fixed and tunable transmitters to assess the trade-off between scheduling complexity and performance. In addition, bidirectionality with one or two transceivers per port is proposed as a way to overcome the throughput degradation caused by the blocking behavior. Simulation results indicate that bidirectionality is very effective in improving the throughput and reducing the latency even when using a single transceiver.

Social Big-Data-Based Content Dissemination in Internet of Vehicles

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

A Fast, Adaptive, and Energy-Efficient Data Collection Protocol in Multi-Channel-Multi-Path Wireless Sensor Networks

A wireless sensor network (WSN) consists of sensor nodes which can self-organize to relay information such as measurements to a base station. To reduce latency and increase data transmission throughput, multi-channel data collection protocols have been proposed to enable simultaneous parallel transmissions between pairs of nodes within the network. However, the existing protocols require long scheduling phase, are less dynamic to network traffic changes, and/or compromise on efficiency by relying on the back-off mechanism such as carrier sense multiple access with collision avoidance (CSMA/CA). This paper proposes a fast, adaptive, and energyefficient data collection protocol in multi-channel-multi-path WSN. The protocol consists of two major phases. The first phase is the node-channel assignment that uses the graph coloring technique to resolve the issue of node overhearing and interference. The second phase is the scheduling and packet forwarding, in which a novel three-dimensional parallel iterative matching (3DPIM) algorithm is proposed to pair up sensor nodes in different time slots so as to enable collision-free multiple simultaneous data transmissions in every time slot. Simulation results show that our proposed protocol can achieve fast and energy-efficient data collection while being adaptive to the change of network traffic in WSN.