Merge Operations Research Articles

A Reconfigurable Architecture for Real-time Event-based Multi-Object Tracking

Although advances in event-based machine vision algorithms have demonstrated unparalleled capabilities in performing some of the most demanding tasks, their implementations under stringent real-time and power constraints in edge systems remain a major challenge. In this work, a reconfigurable hardware-software architecture called REMOT, which performs real-time event-based multi-object tracking on FPGAs, is presented. REMOT performs vision tasks by defining a set of actions over attention units (AUs). These actions allow AUs to track an object candidate autonomously by adjusting its region of attention and allow information gathered by each AU to be used for making algorithmic-level decisions. Taking advantage of this modular structure, algorithm-architecture codesign can be performed by implementing different parts of the algorithm in either hardware or software for different tradeoffs. Results show that REMOT can process 0.43–2.91 million events per second at 1.75–5.45 W. Compared with the software baseline, our implementation achieves up to 44 times higher throughput and 35.4 times higher power efficiency. Migrating the Merge operation to hardware further reduces the worst-case latency to be 95 times shorter than the software baseline. By varying the AU configuration and operation, a reduction of 0.59–0.77 mW per AU on the programmable logic has also been demonstrated.

Environmental inefficiencies for arrival flights at European airports.

In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models.

Multilane freeway merging control via trajectory optimization in a mixed traffic environment

AbstractMerging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.

TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic

Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods.

Generalizing Simultaneously to Support Smooth Zooming: Case Study of Merging Area Objects

When users zoom in or out on a digital map, the map should change correspondingly to present geographical information at proper levels. A way to help map users better keep track of their interested objects is to change the map smoothly instead of discretely switching between several levels of detail. This paper focuses on the problem of providing smooth merging of area objects. We propose to merge multiple areas simultaneously to share their animation durations. In this way, each merging operation can be prolonged, and it is visually smoother. We present a greedy algorithm to decide which areas should be merged at each step. The merging process is pre-computed and is recorded into a space-scale cube (SSC). When a user accesses our web map, the SSC is sent to the client side so that the map can be generated by slicing the SSC in the graphics processing unit (GPU). We also explain how to snap the zooming to valid states so that the zooming will not stop halfway of the merging operations. Our case study shows that it is visually smoother to merge simultaneously than to sequentially merge each pair of areas.

Lite-FPN for keypoint-based monocular 3D object detection

3D object detection with a single image is an essential and challenging task for autonomous driving. Multi-scale feature fusion is effective for keypoint-based monocular 3D object detectors to boost performance within a large range of scales and distances. However, the existing FPN modules inevitably increase latency owing to the further extraction and merging operations on multi-scale feature maps. In this paper, we propose a lightweight feature pyramid network called Lite-FPN for keypoint-based monocular 3D object detectors that perform multi-scale feature fusion only at sparsely distributed keypoint locations. Besides, to alleviate the misalignment between classification score and localization precision, we propose an effective regression loss named attention loss, which assigns predictions with misaligned classification score and localization precision larger weights in the training stage. Extensive experiments based on several state-of-the-art keypoint-based detectors on the KITTI and nuScenes datasets show that our proposed methods manage to achieve significant accuracy improvements. Meanwhile, the enhanced SMOKE with our Lite-FPN module surpasses the baseline enhanced by the classic FPN over 19 FPS.

Comprehensive Review on Capacity Analysis of Rotary Intersection

Rotary intersection, which is a special type of at-grade intersection, where all converging vehicles are forced to move round a central island in clock-wise direction. Increasing trends of traffic in urban area is a major concern in all the cities in India. The heterogeneous traffic are more diverse in nature due to lane changing and lack of lane discipline characteristics of driver’s in India. The rotary intersections are of the most vital components of urban roadway network. Intersection is one when either three or more road meets or intersects each other. It has been observed that the entry capacity of vehicles become comparatively lower at intersection than that of the straight portion of the road due to reduction in speed. The traffic flow characteristics at rotary intersections are study to observe the performance of intersection. The capacity of the roadway rotary depends on the flow at different legs approaching the rotary. The present traffic scenario is usually used to characterize the present traffic condition to access the different parameters at different types of intersection. The crossing of vehicles is avoided by allowing all vehicles to merge into the streams around the rotary and then to diverge out to the desired radiating road. Thus the crossing conflict is eliminated and converted into weaving maneuver or a merging operation from the right and diverging operation to the left. In this paper, an comprehensive review is presented on capacity analysis of rotary intersection.

Bi-level ramp merging coordination for dense mixed traffic conditions

Connected and Autonomous Vehicles (CAVs) hold great potential to improve traffic efficiency, emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles. This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles (HDVs) to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels. The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions, including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed, based on macroscopic traffic state in mainline and ramp (i.e., traffic volume and penetration rates of CAVs). Furthermore, the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon. A receding horizon scheme is implemented to accommodate human drivers’ stochastics as well. The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates. The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness, and reducing collision risk and emissions, especially under high traffic volume conditions.

As-Continuous-As-Possible Extrusion-Based Fabrication of Surface Models

In this study, we propose a computational framework for optimizing the continuity of the toolpath in fabricating surface models on an extrusion-based 3D printer. Toolpath continuity is a critical issue that influences both the quality and the efficiency of extrusion-based fabrication. Transfer moves lead to rough and bumpy surfaces, where this phenomenon worsens for materials with large viscosity, like clay. The effects of continuity on the surface models are even more severe in terms of the quality of the surface and the stability of the model. We introduce a criterion called the one–path patch (OPP) to represent a patch on the surface of the shell that can be traversed along one path by considering the constraints on fabrication. We study the properties of the OPPs and their merging operations to propose a bottom-up OPP merging procedure to decompose the given shell surface into a minimal number of OPPs, and to generate the “as-continuous-as-possible” (ACAP) toolpath. Furthermore, we augment the path planning algorithm with a curved-layer printing scheme that reduces staircase defects and improves the continuity of the toolpath by connecting multiple segments. We evaluated the ACAP algorithm on ceramic and thermoplastic materials, and the results showed that it improves the fabrication of surface models in terms of both efficiency and surface quality.

Barracuda, an Open Source Framework for Parallelizing Divide and Conquer Algorithm

This paper presents a newly-created Barracuda open-source framework which aims to parallelize Java divide and conquer applications. This framework exploits implicit for-loop parallelism in dividing and merging operations. So, this makes it a mixture of parallel for-loop and task parallelism. It targets shared-memory multiprocessors and hybrid distributed shared-memory architectures. We highlight the effectiveness of the framework and focus on the performance gain and programming effort by using this framework. Barracuda aims at large public actors as well as various application domains. In terms of performance achievement, it is very close to Fork/Join framework while allowing end-users to only focus on refactoring code and experts to have the opportunity to improve it.

Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Human activity recognition (HAR) has been applied to several advanced applications, especially when individuals may need to be monitored closely. This work focuses on HAR using wearable sensors attached to various locations of the user body. The data from each sensor may provide unequally discriminative information and, then, an effective fusion method is needed. In order to address this issue, inspired by the squeeze-and-excitation (SE) mechanism, we propose the merging-squeeze-excitation (MSE) feature fusion which emphasizes informative feature maps and suppresses ambiguous feature maps during fusion. The MSE feature fusion consists of three steps: pre-merging, squeeze-and-excitation, and post-merging. Unlike the SE mechanism, the set of feature maps from each branch will be recalibrated by using the channel weights also computed from the pre-merged feature maps. The calibrated feature maps from all branches are merged to obtain a set of channel-weighted and merged feature maps which will be used in the classification process. Additionally, a set of MSE feature fusion extensions is presented. In these proposed methods, three deep-learning models (LeNet5, AlexNet, and VGG16) are used as feature extractors and four merging methods (addition, maximum, minimum, and average) are applied as merging operations. The performances of the proposed methods are evaluated by classifying popular public datasets.

The Impact of FinTech Merge Operation on Financial Performance: Evidence from a Banking International Sample

The FinTech phenomenon has recently had a significant impact on the financial sector, opening up new potential for cost-saving measures and providing increasingly sophisticated financial services. On the other hand, FinTech has helped new players—typically technology companies—enter the financial sector and financial intermediation. Financial institutions have started cooperative and merging operations. As a result, to incorporate the new technology created in the market into their business model. This study aims to determine whether banks engaged in a FinTech merger experience improved financial performance. The research hypothesis is tested using an international sample composed of 106 financial intermediaries that implemented FinTech mergers from 2010 to 2018. The methodology employed is the Propensity-Score-Matching (PSM) technique which provides empirical results using a control group of 8,886 financial firms, with a total of 79,974 observations. The results demonstrate how a FinTech merger enhances intermediaries' financial performance. This evidence highlights the strategic value of FinTech fusion in the modern financial system. This study offers important insights for future research on the topic, as it contributes to combining two distinct outfits of literature, FinTech and M&A, into one that has been little addressed in the financial sector.

Development of multi spindle drill head for portable drilling machine

Productivity and quality in Oman factories are crucial to the development of the industry. Manufacturing efficiency is one of the most important variables in determining an organization's productivity. The total machining time can be reduced by merging operations, etc., which in turn increases productivity. With mass production, speed of production is of the utmost importance because there is a smaller pool of potential workers but a much larger quantity to be made. Using multipurpose machinery to crank out these goods is out of the question. The employment of dedicated machinery is the most effective method for increasing both output (productivity) and quality. As a result of creating and developing a multi spindle drilling head attachment, the current radial drilling machine will be more useful and perform better. This paper discusses the development work done for a similar assignment, and it also includes an industry case study.

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.

Recursive decomposition/aggregation algorithms for performance metrics calculation in multi-level assembly/disassembly production systems with exponential reliability machines

Developing accurate and computationally efficient algorithms for system performance metrics calculation is critical to implementing effective control and optimization in manufacturing system operations. In this paper, we propose a recursive decomposition/aggregation-based method for calculating the performance metrics of assembly/disassembly systems with multiple merge/split operations and sub-assemblies. It is assumed that the machines follow the exponential reliability model and the buffers are of finite capacity. To achieve this, we first consider assembly systems with multiple component lines merging at a single assembly operation. By decomposing the system into a set of virtual serial lines, we derive an analytical procedure to approximate the starvation and blockage probabilities of the merge operation, which are used to recursively update the parameters of the virtual serial lines. Then, the performance metrics of the original assembly system are approximated based on the corresponding machines and buffers in these virtual serial lines. Next, we extend the algorithm to assembly/disassembly systems with multiple merge/split operations and sub-assemblies. This is accomplished by identifying the so-called assembly/disassembly units formed based on the virtual serial lines and applying the calculations derived earlier recursively. Simulation experiments are carried out to justify the convergence, computational efficiency, and approximation accuracy of the proposed algorithms. An industrial case study is presented to demonstrate the theoretical methods in practical applications.

An optimization model for collaborative logistics among carriers in vehicle routing problems with cross-docking

PurposeCollaborative Logistics (CL) and merging operations are crucial strategies for reducing costs and improving service in transportation companies. This study proposes a model for optimizing efficiency in supply chain networks through inbound and outbound Collaborative Logistics implementation among the carriers in centralized, coordinated networks with cross-docking.Design/methodology/approachA mixed-integer non-linear programming model is developed to determine the optimal truck-goods assignment while gaining economies of scale through mixing multiple less-than-truckload (LTL) products with different weight-to-volume ratios. Unlike the previous studies that have considered Collaborative Logistics from the cost and profit-sharing perspective, the proposed model seeks to determine an appropriate form of Collaborative Logistics in the VRP.FindingsThis article shows that in a three-echelon supply chain consisting of a set of suppliers, a set of customers and a cross-docking terminal, partial collaboration among the inbound carriers and outbound carriers outperforms no/complete collaboration. This approach enhances the supply chain efficiency by minimizing the total transportation costs, the total transportation miles and the total number of trucks and maximizing fleet utilization. While addressing the four points, the role of collaborative logistics among the carriers was discussed. In a three-echelon SC consisting of a set of suppliers, a set of customers and a cross-docking terminal, partial collaboration among the inbound carriers and outbound carriers outperforms no/complete collaboration. Using a combination of experimental analysis and optimization process, it was recommended that managers be cautious that too much (full or complete) or no collaboration can result in SC performance deterioration.Originality/valueThe suggested approach enhances the supply chain efficiency by minimizing the total transportation costs, the total transportation miles and the total number of trucks and maximizing fleet utilization. While addressing the four points, the role of Collaborative Logistics among the carriers was discussed.

RFID Landslide Monitoring: Long-Term Outdoor Signal Processing and Phase Unwrapping

Localization of passive Radio-Frequency Identification (RFID) tags has been used to monitor landslide surface displacement since 5 years. This method, applied on slow displacements lower than 1cm per day, allows a high spatio-temporal resolution at a relatively low cost. With the feedback of the previous years, this paper proposes to summarize the various challenges encountered with the long-term outdoor RFID localization method, and presents data-processing solutions that were implemented to overcome these challenges. We propose a complex-smoothing unwrapping algorithm, a multi-frequency merging operation, as well as multi-tag and multi-antenna phase combining method. The concept of an unwrapping reference guide is presented and applied with groups of tags showing coherent displacements, or with absolute reference measurements. These approaches allow a higher data availability up to 38% for one site over multiple years, and a better phase unwrapping. Earth surface displacement monitoring with RFID proves to be a robust and accurate solution, with four equipped sites across France and Switzerland.

Phenomena in Romance verb paradigms: Syncretism, order of inflectional morphemes and thematic vowel

This article aims to propose a treatment of the internal morphological organization of words, based on the idea that morphology is part of syntactic computation. We disagree with Distributed Morphology model, whereby morphology is identified with a post-syntactic component conveying an information ‘separated from the original locus of that information in the phrase marker’ (Embick and Noyer 2001: 557) by rules manipulating syntactic nodes. We also consider inadequate the costly and complex syntactic structures that cartographic approach maps into inflectional strings. We pursue a different conceptualization assuming that morphology is governed by the same rules and principles of syntax. Sub-word elements, including inflections, thematic exponents and clitics, are fully interpretable and enter (pair-)merge operations (in the sense of Chomsky 2020a,b, 2021) according to their content, giving rise to complex words.

A Review of Connected and Automated Vehicle Platoon Merging and Splitting Operations

Connected and automated vehicle (CAV) platoons have drawn much attention in the past decades, given their potential to reduce fuel consumption, elevate roadway capacity, and enhance traffic safety. As two basic platoon operations, platoon merging and splitting have been widely investigated. This study provides an overview of theoretical models and field experiments of CAV platoon merging and splitting operations. A three-step framework, including protocol design, trajectory planning, and vehicle control, is proposed to unify existing representative studies. Methodological techniques in each step are summarized and discussed. Finally, future research directions are discussed. This study contributes to the literature by providing a framework that categorizes relevant literature and guides the successful development of platoon merging and splitting operations. More importantly, it offers researchers and practitioners a rich reference for further investigations.

TRAJECTORY PLANNING ALGORITHM FOR MERGING CONTROL OF HETEROGENEOUS VEHICULAR PLATOON ON CURVE ROAD

The main goal of this study is to propose a trajectory planning algorithm for the merging control of heterogeneous vehicular platoons. Merging control is essential for the application of vehicular platoons, by which vehicles can be coordinated to form a platoon in a lane. While most previous researches on merging control only considered the operation on straight roads and ramps. Few studies have investigated the merging operation on curve roads, which may hinder the application of platoons on general traffic environment. In this study, a trajectory planning algorithm is proposed for the merging control of heterogeneous vehicular platoons on curve roads with constant radius. The proposed algorithm consists of two stages for the operation of merging: the first stage is to align the vehicles in each lane to form a structure with a certain clearance; the second stage is to conduct a lane changing manoeuvre for each merging vehicle to form a platoon in a lane. In the proposed algorithm, the dynamic limits of speed and acceleration are considered. The distance of each vehicle can be guaranteed to avoid undesired collisions. Two simulations are carefully conducted for the merging control of heterogeneous vehicular platoons on a curve road to demonstrate the effectiveness of the proposed algorithm. The results of simulations indicate that the proposed algorithm is capable of the merging control of platoons on a curve road.