Настройка силового контура фронтального погрузчика на площадке с несколькими штабелями остаётся ручной, из-за чего одна и та же машина работает на песке и на разборно-скальном грунте с одинаковыми уставками подачи и тяги. Действующие стандарты описывают результат черпания геометрически: вместимость ковша рассчитывается по ГОСТ 29290-92 через плотную часть и материал над кромками при откосе 2:1, номинальная грузоподъёмность определяется по ГОСТ ISO 14397-1-2015, а коэффициент наполнения ковша не нормирован ни одним из них. Цель работы состояла в построении и расчёте модели системы, опознающей класс материала по силовым и кинематическим сигналам начальной фазы внедрения и назначающей режим работы силового контура без участия оператора. Расчёт выполнен на авторской модели процесса черпания для модельного погрузчика класса 5 т с ковшом вместимостью 3,0 м³; численный эксперимент охватил серию из 1 240 циклов. Материалы сведены в пять классов по значению авторского индекса трудности черпания от 1,00 до 2,35. Предельная глубина внедрения по условию сцепления снижается с 1,35 до 0,88 м, коэффициент наполнения – с 1,06 до 0,76, а масса материала в ковше при этом изменяется всего на 8,7% между сухим песком и щебнем крупной фракции. Класс материала опознаётся по четырём признакам окна 0,4 с с долей верных отнесений 0,914; наибольшая путаница приходится на пару смежных гранулометрических классов. Переход к адаптивному назначению режима увеличивает эксплуатационную производительность на 1,4-10,7% и снижает удельный расход топлива на 12,9-17,4% в зависимости от класса. Выигрыш растёт с трудностью черпания и распределяется между производительностью и топливной экономичностью неодинаково: на связном грунте прирост выработки максимален при наименьшем снижении удельного расхода. Соотношение показывает, что коэффициент наполнения ковша ведёт себя как управляемая переменная, а не как справочная константа машины, и подлежит включению в контур управления наравне с давлением и частотой вращения.
Power train tuning of a front loader operating at a site with several stockpiles remains manual, so the same machine handles sand and blasted rock with identical feed and traction settings. Current standards describe the outcome of the digging phase geometrically: bucket capacity is computed under GOST 29290-92 from the struck volume plus the material above the cutting edges at a 2:1 slope, rated operating load follows GOST ISO 14397-1-2015, and the bucket fill factor is not covered by either document. The purpose of the study was to construct and compute a model of a system that recognises the material class from force and kinematic signals of the initial penetration phase and assigns the power train mode without operator involvement. The computation rests on the author's model of the digging process for a modelled 5 t class loader with a 3.0 м³ bucket; the numerical experiment covered a series of 1,240 cycles. Materials are grouped into five classes by the author's digging difficulty index ranging from 1,00 to 2,35. The limiting penetration depth under the traction condition falls from 1,35 to 0,88 m and the fill factor from 1,06 to 0,76, while the mass carried in the bucket varies by only 8,7% between dry sand and coarse crushed stone. The material class is recognised from four features of a 0,4 s window with a correct assignment share of 0,914, the heaviest confusion falling on the pair of adjacent granulometric classes. Adaptive mode assignment raises operational output by 1,4-10,7% and lowers specific fuel consumption by 12,9-17,4% depending on the class. The gain grows with digging difficulty and splits unevenly between output and fuel economy: on cohesive soil the output gain peaks while the specific consumption gain is smallest. This relation shows that the bucket fill factor behaves as a controlled variable rather than a reference constant of the machine and belongs in the control loop alongside pressure and engine speed.
Михаил Валерьевич Митякин· Stroitelʹnye i dorožnye maši...· 0 citations
This paper introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture.
Pradeep Kachakayala, Akshith Kachakayala· International Journal for Re...· 0 citations
This review aims to examine how AI can help optical sensors overcome major barriers limiting their adoption in clinical settings and to identify the major barriers limiting their adoption in clinical settings.
Siyi Zeng, Haoyu Li, Guoliang Ying et al.· Translational Medical Engine...· 0 citations
The massive influx of uplink task offloading in Multi-access Edge Computing (MEC) systems poses a significant challenge to the capacity of wireless networks. This challenge highlights a fundamental trade-off between Orthogonal Multiple Access (OMA), which provides interference-free but spectrally inefficient communication, and Non-Orthogonal Multiple Access (NOMA), which enhances capacity at the cost of significant inter-user interference. To navigate this trade-off, we introduce a novel Hybrid NOMA (H-NOMA) framework that offers differentiated communication services. The framework allows users to choose between premium OMA channels for latency-sensitive tasks and shared NOMA channels for others, creating an economy where performance can be traded for cost. Within this framework, we formulate the resource allocation problem with the objective of maximizing the total system utility, defined as the sum of all individual user utilities, under budget, computation, and communication constraints. To solve this NP-hard problem, we devise a novel multi-stage game-theoretic algorithm, the Matching-Coalition Game with Coordinate Descent (MCGCD). Our approach synergistically combines matching theory for a fast and initial channel assignment, a cooperative coalition game to refine allocations by explicitly managing NOMA externalities, and a coordinate-descent-based algorithm for optimal power control. Extensive simulations demonstrate that our proposed algorithm significantly outperforms benchmark methods in improving system utility, reducing average task completion latency, and increasing the number of admitted tasks.
Haolin Liu, Hao Yin, Haibo Zhou et al.· IEEE Transactions on Mobile...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper presents a reliability analysis framework for distributed computing in extreme edge computing (XEC) with limited information availability. XEC pushes computation to the outermost boundaries of networks by leveraging consumer-owned devices, known as Extreme Edge Devices (XEDs). Unlike traditional distributed systems with defined computational resource states, XEC operates under uncertainty due to consumer device usage patterns, varying computational capacities, and local scheduling algorithms. In this work, we address discrete task assignment particularly. The framework analyzes scenarios for computational reliability assessments with minimal knowledge of XED capabilities and service requirements. The framework adapts to different levels of available information, from operational limits to historical performance data, providing refined reliability estimates. The aim of this work is to provide generalized reliability models for distributed computing in XEC that allow decision-makers (e.g. service orchestrators) to make informed decisions about task allocation, service placement, and resource allocation under uncertainty in XEC. Simulations and experimental analysis demonstrate the framework’s effectiveness in estimating reliability under various system conditions.
Mhd Saria Allahham, Hossam S. Hassanein· IEEE Transactions on Mobile...· 0 citations
Cohesive subgraph mining has been extensively studied and finds numerous graph mining applications such as link farm identification, community detection, and product recommendation. Among various cohesive subgraph structures, the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq3-3701433.gif"/></alternatives></inline-formula>-truss is particularly notable for its strong structural cohesiveness based on triangles. However, the classical <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq4-3701433.gif"/></alternatives></inline-formula>-truss problem aims to find the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq5-3701433.gif"/></alternatives></inline-formula>-truss with the maximum number of vertices, which is often extremely large and complex in practice. To fully leverage the benefits of the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq6-3701433.gif"/></alternatives></inline-formula>-truss, we consider a novel problem called the <italic>minimum <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq7-3701433.gif"/></alternatives></inline-formula>-truss problem</italic>, which seeks to identify a <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq8-3701433.gif"/></alternatives></inline-formula>-truss with the minimum number of vertices, where <inline-formula><tex-math notation="LaTeX">$k\geq 2$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>≥</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq9-3701433.gif"/></alternatives></inline-formula> is a positive integer. We first formally prove the NP-hardness of the problem. We then design a baseline algorithm <monospace>MTEnum</monospace> that is based on the vertex enumeration and a heuristic method for computing an upper bound. Despite these efforts, <monospace>MTEnum</monospace> still faces practical efficiency issues which may be due to the fact that the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq10-3701433.gif"/></alternatives></inline-formula>-truss lacks the hereditary property. To address this issue, we develop a novel decomposition-based framework <monospace>DSA</monospace>, which elegantly transforms the problem into a sequence of problems that are based on a new cohesive subgraph model called <italic>edge-based <inline-formula><tex-math notation="LaTeX">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq11-3701433.gif"/></alternatives></inline-formula>-plex (<inline-formula><tex-math notation="LaTeX">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq12-3701433.gif"/></alternatives></inline-formula>-eplex)</italic>. With the hereditary property of <inline-formula><tex-math notation="LaTeX">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq13-3701433.gif"/></alternatives></inline-formula>-eplex, we design a branch-and-bound algorithm with several customized techniques for the newly formulated problem. Extensive experiments demonstrate the effectiveness of our studied problem and the efficiency of our proposed algorithm <monospace>DSA</monospace>. In particular, <monospace>DSA</monospace> runs up to five orders of magnitude faster than the baseline <monospace>MTEnum</monospace>.
Qifan Zhang, Yang Liu, Kaiqiang Yu et al.· IEEE Transactions on Knowled...· 0 citations
Advancements in edge computing and container technology have made it increasingly popular and convenient to deploy Large Language Models (LLMs) through containers at the edge. However, the limited GPU resources of edge servers make it impractical to retain the model in GPU memory for long periods due to the high memory cost, especially when they remain idle without user requests. Existing work unloads the entire idle models to reduce memory costs on edge servers, but reloading them introduces significant loading delays that affect task Quality of Service (QoS). Therefore, efficient management of idle models is a critical issue that has been largely neglected in existing research and requires urgent attention. To address this gap, this paper studies the problem of idle model management from the perspective of the trade-off between memory cost and loading delay under the QoS constraint. A novel layer-granularity model unloading method is proposed, which leverages the layered characteristics of the model. We formulate an online joint optimization problem to determine which layers to unload and when, and present a layer-granularity unloading strategy inspired by the ski rental problem to solve it. We implement a real system with layer-granularity unloading for LLMs on NVIDIA GPUs and validate the effectiveness of the proposed method. Experimental results show it effectively trades off memory cost and loading delay, improving overall performance by up to 39.6%.
Zhenzheng Li, Zhiqing Tang, Jianxiong Guo et al.· IEEE Transactions on Mobile...· 1 citation
Dispersed computing has emerged as a promising paradigm that leverages underutilized resources from massive Internet of Things devices (IoTDs) to enhance the computing capacity at the network edge. However, existing works about the dispersed computing overlook the heterogeneous computing environment with parallel and serial computations and task reliability requirements for the hardware-constrained IoTDs, and they lack multi-objective optimization approaches to optimize the task offloading. To address the challenges, we propose a comprehensive scheme to achieve a delay-aware and economic-aware dispersed computing paradigm by using a multi-objective optimization approach. Particularly, we consider parallel processing at an edge server and serial processing at the lightweight IoTDs, and leverage the task redundancy to satisfy the task reliability requirements on the IoTD side. We further formulate a constrained multi-objective optimization problem (CMOP) aiming at jointly optimizing the task assignment, bandwidth allocation, and CPU frequency allocation to simultaneously minimize the total delay cost and the total charge cost of the tasks. To address the CMOP, we propose an improved constrained multi-objective evolutionary algorithm that employs a dual-population cooperative mechanism between two populations and a repairing constraint-handling technique. The dual-population cooperative mechanism can balance convergence toward Pareto optimality and solution diversity maintenance. The repairing constraint-handling technique is designed to guide solutions toward feasible regions, achieving efficient exploration of complex constrained search spaces. Simulation results demonstrate the superiority of our algorithm in seeking the better-converged and better-distributed Pareto optimal solutions to well address the tradeoffs between the two objectives.
Xumin Huang, Zexiong Wu, Chaoda Peng et al.· IEEE Transactions on Mobile...· 2 citations
We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, including which tasks need to be processed by uncrewed aerial vehicles (UAVs), how to allocate these tasks and balance energy across UAVs for delay-sensitive requirements. However, little attention has been devoted to exploring the above coupled decision-making problem in AEC with various resource and energy constraints, which is further complicated by energy dynamics (UAV battery states), task-specific consumption, and allocation-feedback balance. In this paper, we formulate a multi-dimensional joint optimization problem, simultaneously optimizing task allocation and energy rewarding to maximize long-term system rewards while balancing service quality and energy efficiency. To this end, we propose a green aerial edge computing framework where partial UAVs are equipped with energy harvesting modules to collect ambient energy. To circumvent the intractable computational complexity arising from the coupled energy states of massive UAVs, we design a distributed solution method based on the mean field game, which decouples the dense multi-agent interactions into a game between an individual UAV and the aggregate population state, thereby transforming the complex global optimization problem into a set of equivalent scalable subproblems. We develop an optimal energy valuation scheme to guide UAV behavior. Numerical results show that our mechanism can effectively ensure sustainable system operation while maintaining high quality of service for metaverse users, outperforming existing methods in both system sustainability and service responsiveness.
Lianbo Ma, Dingsige Chen, Yuee Zhou et al.· IEEE Transactions on Mobile...· 0 citations
To address the urgent practical challenge of service migration for connected autonomous vehicles (CAVs) in mobile edge computing (MEC), this study aims to maximize the task completion rate, particularly for safety-critical operations. Existing approaches often overlook the completion status of tasks with different priority levels during frequent service migrations and fail to co-optimize multiple constraints such as energy consumption, latency, and offloading cost. Consequently, it remains difficult to reliably complete highly urgent tasks in resource-constrained edge environments. To tackle this issue, we propose a comprehensive two-stage solution: the Improved Task Offloading and Service Migration (ITOSM) algorithm. In the first stage, a weighted evaluation model based on information entropy is constructed by integrating transmission time, execution time, and offloading cost. Tasks are offloaded to the edge server with either the highest or second-highest weighted sum according to their urgency level. In the second stage, service migration decisions are optimized using an improved binary particle swarm optimization (BPSO) algorithm with enhanced local search capability. Experimental results demonstrate that ITOSM outperforms existing methods, achieving up to 10.00% higher completion rates for Extremely Important Tasks (EITs) with strict deadlines. This improvement directly contributes to safer and more reliable CAV operations, highlighting the practical significance of this work for intelligent transportation systems.
As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction-based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs’ preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.
Dongkuo Wu, Xingwei Wang, Xueyi Wang et al.· IEEE Transactions on Mobile...· 0 citations
Network Function Virtualization (NFV) is a foundational technology for Mobile Edge Computing (MEC). It delivers network services by chaining Virtual Network Functions (VNFs) into sequential Service Function Chains (SFCs). One of the most critical challenges in MEC is how to provide continuous and stable services to high-mobility user, such as intelligent vehicles and drones. However, current mobility-aware SFC migration methods remain constrained by either post-hoc reaction or myopic prediction horizons, failing to reconcile the divergent timescales of network services and user mobility, thus resulting in suboptimal resource allocation and service delivery. In this paper, we first formulate the predictive mobility-aware SFC migration problem as an NP-hard Integer Linear Programming (ILP) problem. Aiming to mitigate service disruption for mobile users in MEC networks, we propose PreSFC, a predictive SFC migration framework that integrates multi-slot mobility forecasting with fine-grained network state tracking. We first design Gformer, a deep learning-based long sequence time-series forecasting model, which operates on short time slots (less than 200 ms) to sensitively capture network dynamics while predicting over multiple slots (e.g., 50 slots) to effectively track user mobility. This dual-scale design explicitly addresses the temporal disparity between mobility patterns and service requirements. Based on the predictions, we further propose an Optimal Sub-period Partitioning Migration (OSPM) algorithm to determine migration timing and locations. Extensive simulations show that our approach reduces the maximum and average downtime by approximately 55% and 40%, respectively, compared to benchmark methods.
Ji Li, Songtao Guo, Quanjun Zhao et al.· IEEE Transactions on Mobile...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
