Dynamic Matching Research Articles

Exploring the nexus between inflation targeting and exchange market pressure: Evidence from the global financial crisis

This study investigates the effects of the inflation targeting (IT) monetary framework on the exchange market pressure index (EMPI) across 24 Asian economies from 1998 to 2018, with a particular focus on the period covering the global financial crisis (2008–2018). Employing dynamic panel data modeling and Propensity Score Matching, our analysis reveals a positive impact of implementing the IT on the EMPI. The findings suggest that adopting an IT regime effectively reduces exchange market pressure, demonstrating notable resilience following the global financial crisis of 2008. This highlights that an IT policy enhances the credibility of monetary policy, thereby decreasing the need for central bank interventions in the foreign exchange market within this framework.

Visual object tracking based on adaptive rain streak removal and fused dynamic template matching

Visual object tracking based on adaptive rain streak removal and fused dynamic template matching

Bidirectional Elastic PTFE Small Diameter Artificial Blood Vessel Grafts and Surface Antithrombotic Functionalized Construction.

Expanded polytetrafluoroethylene (ePTFE) failed to achieve clinical application in the field of small-diameter blood vessels due to its lack of elasticity in the circumferential direction and high stiffness. Excellent multidirectional elasticity and dynamic compliance matching with natural blood vessels are important means to solve the problem of acute thrombosis and poor long-term patency. Herein, novel PTFE spinning blood vessels were prepared by the PTFE emulsion electrospinning process, which not only presented good bidirectional elasticity but also promoted the adhesion and proliferation of endothelial cells and induced the contractile expression of SMCs. And, a PTFE-shish and aminated polycaprolactone (PCL)-kebab structure has been developed that converted the chemically inert PTFE surface into a drug-loading platform for the multifunctionalization of PTFE vascular grafts. It provides novel preparation methods for the application of new bidirectional elastic small-diameter artificial blood vessels and their surface functionalization construction.

Dynamic matching of energy and heat conduction in production workshops and financing for manufacturing enterprises based on the Internet of Things

With the advancement of Industry 4.0, the application of Internet of Things technology in manufacturing enterprises is increasingly extensive, and energy heat transfer, as an important factor affecting production efficiency and cost, needs to be optimized by new technical means. This study explores the energy heat conduction characteristics of production workshops based on the Internet of Things and its impact on the dynamic matching of financing for manufacturing enterprises, providing an innovative energy management model to promote the sustainable development of enterprises. The research uses the Internet of Things technology to monitor the energy flow and heat transfer in the production shop in real time through sensors and data acquisition systems. Combined with big data analytics and machine learning algorithms, in-depth analysis of energy consumption data is performed to identify bottlenecks and optimization points for heat transfer. At the same time, the financing dynamic matching model is constructed to analyze the financing needs and costs of enterprises under different energy management levels. The study found that iot technology can significantly improve the thermal efficiency of production plants and reduce energy waste. On the basis of the optimization of energy heat transfer, the financing demand and financing cost of enterprises have also undergone dynamic changes. The optimization of energy heat transfer based on the Internet of Things not only improves the energy efficiency of the production floor, but also provides a new impetus for the financing of manufacturing enterprises. Implementing an effective energy management strategy will help companies stay ahead of the curve in an increasingly competitive market.

Dynamic entrainment: A deep learning and data-driven process approach for synchronization in the Hodgkin-Huxley model.

Resonance and synchronized rhythm are significant phenomena observed in dynamical systems in nature, particularly in biological contexts. These phenomena can either enhance or disrupt system functioning. Numerous examples illustrate the necessity for organs within the human body to maintain their rhythmic patterns for proper operation. For instance, in the brain, synchronized or desynchronized electrical activities can contribute to neurodegenerative conditions like Huntington's disease. In this paper, we utilize the well-established Hodgkin-Huxley (HH) model, which describes the propagation of action potentials in neurons through conductance-based mechanisms. Employing a "data-driven" approach alongside the outputs of the HH model, we introduce an innovative technique termed "dynamic entrainment." This technique leverages deep learning methodologies to dynamically sustain the system within its entrainment regime. Our findings show that the results of the dynamic entrainment technique match with the outputs of the mechanistic (HH) model.

A New Joint Training Method for Facial Expression Recognition with Inconsistently Annotated and Imbalanced Data

Facial expression recognition (FER) plays a crucial role in various applications, including human–computer interaction and affective computing. However, the joint training of an FER network with multiple datasets is a promising strategy to enhance its performance. Nevertheless, widespread annotation inconsistencies and class imbalances among FER datasets pose significant challenges to this approach. This paper proposes a new multi-dataset joint training method, Sample Selection and Paired Augmentation Joint Training (SSPA-JT), to address these challenges. SSPA-JT models annotation inconsistency as a label noise problem and selects clean samples from auxiliary datasets to expand the overall dataset size while maintaining consistent annotation standards. Additionally, a dynamic matching algorithm is developed to pair clean samples of the tail class with noisy samples, which enriches the tail classes with diverse background information. Experimental results demonstrate that SSPA-JT achieved superior or comparable performance compared with the existing methods by addressing both annotation inconsistencies and class imbalance during multi-dataset joint training. It achieved state-of-the-art performance on RAF-DB and CAER-S datasets with accuracies of 92.44% and 98.22%, respectively, reflecting improvements of 0.2% and 3.65% over existing methods.

Artificial Intelligence (AI)-Powered Piracy

This research paper explores the impact of artificial intelligence (AI) on piracy in the digital environment. It looks at how piracy has increased in the digital era and how AI may help protect intellectual property and provide pirates new strategies. The use of AI in content piracy is covered in the study, including the creation of text and audio, deepfake films, and counterfeit goods. Additionally, it looks at AI-assisted piracy technologies including dynamic content matching, automated content distribution and scraping, and content monetization.

Energy and parametric optimization analysis of a novel double serpentine runner air-cooled PV/T assisted variable capacity air source heat pump system

Through different technologies, the solar energy and air source energy have been effectively used as renewable energies to address the heating problem in the cold area of Northwest China. However, there are very few researches on the comprehensive performance of heat transfer enhancement and airflow pressure drop in the solar collector, as well the dynamic matching performance between the air source heat pump system and energy load of building. To address the mentioned problems, we propose a novel double serpentine runner air-cooled photovoltaic/thermal collector assisted variable capacity air source heat pump (DSPV/TSAC-VCASHP) system. The summary of experimental and numerical results of the heat transfer characteristics of the double serpentine runner was used in the simulation model of the novel collector. The collector and air source heat pump system are connected in parallel with building room for space heating. The dynamic simulation model of the system was established and simulated using TRNSYS to study and analyze the power consumption, effect of key parameters, as well seasonal performance of the novel system. The simulated results show that the system could reach around 88 % power consumption lower than that of single air source heat pump system during the heating season.

A novel optimal temporal lag air2stream model using dynamic time series matching

ABSTRACT In order to solve the problem that the current air2stream model does not consider the lag change of water temperature, this paper proposes a new method that combines empirical mode decomposition, power spectral density and dynamic time warping algorithm to determine the optimal time lag parameters, and then constructs the air2stream with the optimal time lag (a2swtl). Finally, the effectiveness and stability of the new method and model were verified based on the measured data from 2020-2022 at three hydrological stations in the middle and lower reaches of the Yangtze River that are close to natural river conditions. The results show that the new method overcomes the problem of low accuracy in determining the time lag of the existing correlation coefficient method. Compared with the original model, a2swtl has better stability without introducing additional observation data, and the average prediction accuracy is improved by about 6.33%. 8.36% and 15.4%.

A 0.075 mm2 BJT-based temperature sensor with a one-point trimmed 3[formula omitted] inaccuracy of ±0.97 °C from −40 °C to 120 °C

This paper presents a bipolar junction transistor (BJT)-based CMOS temperature sensor for high accuracy, small-scale area, and low power consumption. A structure with a feedback amplifier biasing NPN transistors, combined with dynamic element matching (DEM), is proposed to avoid the effects of errors arising from the limited current gain of substrate PNP transistors in deep-submicron processes. Moreover, the switched capacitor (SC) integrators employ two single-stage cascode amplifiers for alternating cyclic sampling and integration, effectively simplifying the circuit design and reducing the operating voltage. The proposed sensor is fabricated with a standard 180 nm CMOS process, occupying an active chip area of 0.075 mm2. It consumes 39.1 μW of power at room temperature, operating with a supply voltage of 1.8 V. The measurements indicate that the sensor exhibits an inaccuracy of ±0.97 °C (3σ) across the temperature range from −40 °C to 120 °C following a single-point temperature calibration.

Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning

Multivariate machining quality prediction of thin-walled parts with multiple machining features is a complex problem due to different data distribution between training and unlabelled test samples. Traditional quality prediction methods ignore the correlation of multiple quality labels and do not consider changes in data distribution, resulting in low accuracy of multivariate quality prediction. Therefore, a multivariate quality prediction method using multi-task parallel deep transfer learning is proposed to solve this problem. Specifically, a multi-output quality prediction model of cross-machining features is constructed through the joint design of multi-task parallel learning and deep transfer learning. Furthermore, a domain matcher is designed to form multiple transfer strategies, which can be used for dynamic matching of multi-source and multi-target machining features with multiple quality labels. The domain invariant data features through dynamic domain adaptation are extracted to deal with data distribution discrepancy between the source and target domains. Finally, the results of multiple comparison experiments show that the proposed method can effectively achieve the accurate quality prediction of the target domain with unlabelled labels and different distributions. Compared with the traditional methods, the proposed method has improved by 8.34%, 7.14%, and 9.09%, respectively, in MAE, RMSE, and score on average.

A two-stage optimal operation strategy for community integrated energy system considering dynamic and static energy characteristics

The community integrated energy system (CIES) can provide users with reliable and economical energy supply through centralized utilization of various energy equipment. However, due to the significant difference in inertia between different energy sources, the response speed of equipment using different energy sources varies greatly, making it difficult to cooperate well and limiting their ability to cope with source-load uncertainty. Based on this consideration, a two-stage optimal operation strategy for CIES is proposed to utilize dynamic and static energy separately. First, the dynamic and static energy are defined and divided, and their different functions are analyzed. Based on this, a two-stage optimal operation architecture is proposed. Then, a day-ahead interval optimal operation model considering dynamic and static energy matching is established. In which, the uncertainty of source and load is characterized by nonlinear interval numbers, and the interval possibility degree is used for deterministic transformation of nonlinear interval numbers. Thirdly, a dynamic and static energy replacement mechanism based on model predictive control is given, and an intra-day rolling optimal operation model considering dynamic and static energy replacement is established. Finally, a CIES is used as an example to demonstrate the rationality and effectiveness of the proposed strategy. Simulation results show that the proposed strategy can effectively utilize different types of energy equipment and significantly reduce the adverse effects of source-load uncertainty.

DGM-Flow: Appearance flow estimation for virtual try-on via dynamic graph matching

A virtual-image try-on is aimed at warping the target garment image to align with structure of the human body. In recent studies, flexible flow field estimation has been employed via feature matching between the garment and the human body to simulate garment deformation. However, currently available methods for estimating appearance flow rely on a direct comparison of feature descriptors between the source garment and the target pose, while neglecting the surrounding information. This limitation is prone to noisy feature matching, resulting in low accuracy for complex deformations. To overcome this challenge, herein we propose a novel framework named DGM-Flow that exploits the neighbor structure invariance of key features for feature matching, thus improving the estimation accuracy of the appearance flow. Specifically, DGM-Flow models higher-order geometric similarity information between the source garment and the target pose via dynamic graph matching. Moreover, considering the incomplete mapping of garment features before and after deformation, DGM-Flow is used to estimate a hard attention mask matrix to filter irrelevant features using cross-graph matching. Experiments conducted on both the VITON and VITON-HD datasets revealed that DGM-Flow affords better performance than the current leading algorithms, especially when dealing with intricate clothing deformations.

Energy-efficient maximization algorithm for energy harvesting under imperfect channel information

In this paper, we propose a novel energy-efficient resource optimization scheme in non-orthogonal multiple access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT). In this system, we consider practical imperfect channel information that accounts for random channel delays and channel estimation errors. Additionally, the small cell users (SCUs) in a heterogeneous network (HetNet) can harvest energy from the small base station (SBS) signal by energy harvesting. Based on the non-linear energy harvesting (NEH) model, the problem of maximizing energy efficiency with imperfect channel state information (CSI) is formulated. Since the formulated problem is a probabilistic mixed non-convex optimization problem, a two-stage algorithm is developed to jointly optimize sub-channel matching and power allocation. In particular, the problem is first transformed into a non-probabilistic problem through the relaxation method. For the sub-channels matching problem, a low-complexity many-to-many matching algorithm is designed to achieve dynamic matching based on the preference lists. For the power allocation problem, the closed-form transmission power of SCUs can be derived by the Dinkelbach method and Lagrangian dual approach. Simulation results show that the proposed scheme can achieve higher energy efficiency than existing linear energy harvesting (LEH)-NOMA and NEH-OFDMA schemes, with improvements of 37.99% and 84.69%, respectively.

Ocean wave energy harvesting with high energy density and self-powered monitoring system

Constructing a ocean Internet of Things requires an essential ocean environment monitoring system. However, the widely distributed existing ocean monitoring sensors make it impractical to provide power and transmit monitored information through cables. Therefore, ocean environment monitoring systems particularly need a continuous power supply and wireless transmission capability for monitoring information. Consequently, a high-strength, environmentally multi-compatible, floatable metamaterial energy harvesting device has been designed through integrated dynamic matching optimization of materials, structures, and signal transmission. The self-powered monitoring system breaks through the limitations of cables and batteries in the ultra-low-frequency wave environment (1 to 2 Hz), enabling real-time monitoring of various ocean parameters and wirelessly transmitting the data to the cloud for post-processing. Compared with solar and wind energy in the ocean environment, the energy harvesting device based on the defective state characteristics of metamaterials achieves a high-energy density (99 W/m3). For the first time, a stable power supply for the monitoring system has been realized in various weather conditions (24 h).

Dealiased seismic data interpolation by dynamic matching

Interpolation is a critical step in seismic data processing. Gaps in seismic traces can lead to severe spatial aliasing phenomena in the corresponding f- k spectra. The aliasing caused by regularly spaced gaps has similar f- k spectra as those of the actual data. Existing dealiasing interpolation algorithms generally assume that seismic events are linear and cannot handle nonstationary events. To address this shortcoming, we develop a novel dealiased seismic data interpolation approach using dynamic matching. First, we match two adjacent seismic traces using the local affine regional dynamic time-warping algorithm. Subsequently, we calculate the local slope between two seismic traces. Finally, we perform linear interpolation on the regularly missing seismic data using local slope information. Our approach is tested on synthetic and field seismic data sets. The interpolation results indicate that our approach has a better antialiasing ability and computational efficiency than the traditional Spitz and seislet-based approaches. In addition, this method can be applied to interpolate irregularly sampled seismic data and for simultaneous seismic data interpolation and denoising.

Dynamic power matching for wheel loader based on power reflux hydrodynamic transmission system

Dynamic power matching for wheel loader based on power reflux hydrodynamic transmission system

The Cost of Impatience in Dynamic Matching: Scaling Laws and Operating Regimes

We study matching queues with abandonment. The simplest of these is the two-sided queue with servers on one side and customers on the other, both arriving dynamically over time and abandoning if not matched by the time their patience elapses. We identify nonasymptotic and universal scaling laws for the matching loss due to abandonment, which we refer to as the “cost of impatience.” The scaling laws characterize the way in which this cost depends on the arrival rates and the (possibly different) mean patience of servers and customers. Our characterization reveals four operating regimes identified by an operational measure of patience that brings together mean patience and utilization. The four regimes subsume the regimes that arise in asymptotic (heavy-traffic) approximations. The scaling laws, specialized to each regime, reveal the fundamental structure of the cost of impatience and show that its order of magnitude is fully determined by (i) a “winner-take-all” competition between customer impatience and utilization, and (ii) the ability to accumulate inventory on the server side. Practically important is that when servers are impatient, the cost of impatience is, up to an order of magnitude, given by an insightful expression where only the minimum of the two patience rates appears. Considering the trade-off between abandonment and capacity costs, we characterize the scaling of the optimal safety capacity as a function of costs, arrival rates, and patience parameters. We prove that the ability to hold inventory of servers means that the optimal safety capacity grows logarithmically in abandonment cost and, in turn, slower than the square-root growth in the single-sided queue. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01513 .

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.

Mobile Apps, Trading Behaviors, and Portfolio Performance: Evidence from a Quasi-Experiment in China

Mobile Apps, Trading Behaviors, and Portfolio Performance: Evidence from a Quasi-Experiment in China How do mobile apps influence individual investors’ financial decisions and performance? This study answers this timely and important question by using rare archival data from a large securities company in China using a sample of 20,665 investors. Authors find that mobile app adoption does not affect investors’ portfolio performance when one examines aggregate impacts using a binary indicator of mobile app use. Their additional analyses suggest that adopting mobile apps results in a noticeable decrease in time constraints, a proxy for transaction friction, and a modest increase in trend-chasing bias, reflecting tendencies toward myopic decision making. Because the reduction in time constraints can benefit investors’ performance, the increase in trend chasing can be detrimental to investors’ performance, these findings explain why mobile app adoption has no overall effect on portfolio performance. Further analyses of adopters’ postadoption behaviors provide interesting insights and show that the mobile app usage intensity has an inverted U–shaped relationship with portfolio performance. The results are robust to using different samples or excluding high market volatility periods and by using a variety of methods, such as propensity score matching, dynamic matching, stacked difference-in-differences, or an instrumental variable approach.