Placement Algorithm Research Articles

Broomrocket: Open Source Text-to-3D Algorithm for 3D Object Placement

Story writers and other creative professionals often rely on concept artists to visualize and then further iterate on their work during game development and other visualization processes. This exchange and its various stages are time-consuming, and there is no easy remedy for creating a walkable 3D concept art without involving a 3D artist yet. As a first step, we present Broomrocket, an open source text-to-3D algorithm for 3D concept art. Broomrocket’s contribution is an object relation and placement algorithm that transforms user input describing a 3D scene given in plain English language into actual models placed in a 3D scene. It runs locally using an existing downloaded natural language processing model and does not require third party services unless a connection to an online 3D model distribution platform is desired. In that case, Broomrocket will search for the keywords from the user’s narrative input and desired license, and place them in the 3D scene, adding each model’s individual license to a license file for further usage.

Proportional-retarded control of a quasi-zero-stiffness vibration isolator

This paper explores the control design and performance analysis of an active quasi-zero-stiffness vibration isolator utilizing the proportional-retarded (PR) control architecture. The PR architecture replaces the conventional “derivative” term in the proportional-derivative (or displacement-velocity) controller with the novel “retarded (time-delayed)” term, which is a new and effective way of active vibration suppression. Comprehensive gain-delay conjoint regulation strategies are developed for the strengthened isolation performance. For impact excitations, a complete stability analysis is conducted near the equilibrium position. Furthermore, a parameterized dominant pole placement algorithm is proposed to optimize the transient response of the PR-actuated isolator. Focusing on the rectangular pulse and long-duration step impacts, the algorithm adjusts the dominant poles to optimize the transient performance metrics, including the maximum displacement overshoot, steady-state displacement overshoot, and settling time. Regarding harmonic excitations, the delay-coupled effect is scrutinized for better low-frequency isolation performance, including the resonance frequency, nonlinear steady-state amplitude, multiple steady-state bands and nonlinearity elimination, equivalent active damping and stiffness, and absolute displacement transmissibility. In the case of random excitations, we demonstrate the superiority of the PR structure over the passive system.

Intelligent service placement algorithm based on DDQN and prioritized experience replay in IoT-Fog computing environment

The widespread use of Internet of Things-based applications has led to the development of the fog computing paradigm, enabling seamless utilization of edge and cloud resources. The placement of IoT service requests at the appropriate fog nodes is one of the key problems in a fog-cloud environment, which affects the overall system performance and user experience. The challenges associated with service placement are the stochastic nature of service demand, heterogeneous and distributed characteristics of fog nodes. The objective of this work is to develop an effective service placement method to meet the varying resource requirements of services while optimizing two important performance parameters: service execution time and fog nodes’ energy consumption. Existing solutions are slow to adapt and neglecting the temporal patterns of service resource demand. This paper proposes a novel service placement algorithm based on double deep Q-Network and prioritized experience replay (DDQN-PER), which explores the optimal service placement strategy using the adaptive learning ability of deep reinforcement learning (DRL). The DRL agent learns the dynamic nature of the resource requirements of services to find an appropriate fog node for service placement. To evaluate the performance of the proposed algorithm, experiments are carried out using Measurement and Analysis on the WIDE Internet (MAWI) traffic traces. The proposed DDQN-PER algorithm, compared with other state-of-the-art methods and experimental results, show that the proposed method can minimize the service latency time and fog node’s energy consumption from 2.5% to 8% and 6% to 17%, respectively.

Stability constraints in a 3D knapsack problem with non parallelepipedic items

This paper concerns stability issues that we encountered while solving an industrial pallet loading problem. The practical problem can be modeled as a three-dimensional knapsack with original constraints related to the stability of the pallet: when the placement of the boxes on the pallet is computed, the weight of the boxes and the placement sequence are not known.Despite this lack of information, the placement must remain stable throughout the construction of the pallet. Even more important, the major difference with classical placement problems is that the boxes are parallelepipedic rectangle with edge reduction (the upper surface may be smaller than the bottom face).In this paper, we review stability constraints used in the literature and the assumptions under which these constraints can be used and we detail their adequacy to the problem we consider. We then propose a new stability constraint which takes into account the practical features, which can be integrated into a placement algorithm and which is not too restrictive.Numerical experiments on benchmarks from the literature show that this constraint, added to a classical placement algorithm, obtains results which are similar to algorithms from the literature dedicated to a specific stability constraint for parallelepipedic boxes. By adjusting the benchmark to take into account the practical problem specificities, we show that this new stability constraint is perfectly suited to the industrial problem and obtains very good results compared to classical constraints. Therefore, it has been integrated in the placement software developed by the company.

Opposition‐based improved memetic algorithm for placement of concurrentInternet of Thingsapplications in fog computing

AbstractThe transpiration of a new computing standard, Fog computing exploits the computing resources immediacy to the Internet of Things (IoT) devices and hence together with the cloud servers bestow the required resources promptly. Even though the fog server provides timely services due to the fewer resources it cannot tackle the resource‐consuming applications developed by more IoT devices. Hence the placement of applications in the fog computing paradigm with more cloud and fog servers is a major challenging problem. To overcome this problem and to minimize the execution time and energy consumption of the IoT applications in a computing platform consisting of numerous IoT devices, and several fog and cloud servers, a weighted cost model is proposed. The number of parallel jobs for concurrent execution is increased due to the heterogeneity of IoT applications, and hence a pre‐scheduling technique is presented to accomplish this. In addition to that, a novel application placement method using the Opposition‐based Improved Memetic Algorithm (OBIMA) is adapted to formulate the placement of parallel IoT operations. The experimental results show that the proposed model outperforms well compared to other state‐of‐the‐art methods.

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

An autonomous ore packing system through deep reinforcement learning

In the contemporary era, the limited availability of terrestrial resources has prompted an increasing number of nations to turn their attention towards space, wherein extraterrestrial minerals hold considerable allure for both resource provisioning and scientific inquiry. Numerous nations have initiated the deployment of unmanned operational platforms towards extraterrestrial asteroids with the objective of accomplishing sampling return missions. Given the restricted storage capacity and energy limitations of these platforms, the optimization of mineral placement algorithms assumes paramount importance in enhancing the efficacy of these missions. In this paper, we propose an autonomous ore packing system capable of autonomously measuring ore characteristics and addressing the ore packing optimization problem in extraterrestrial unfamiliar environments. A deep reinforcement learning method that utilizes physical constraints to enhance overall performance was proposed to solve the ore packing problem with uncertainty, while meeting the high demands for real-time performance in the mission. To augment the autonomy and adaptability of our approach, we leverage advanced visual technology to transform the spatial distribution of bin utilization and the characteristics of the ore into a matrix representation. This empowers the robotic system to autonomously perceive bin information and capture essential ore features. The empirical findings substantiate that our algorithm attains a human-level performance in the majority of instances, rendering an approximate optimal solution within a concise timeframe. Additionally, we introduce a novel reinforcement learning training technique known as Maximum Worth Reinforcement Learning (MWRL) to address the optimization conundrum associated with incomplete Markov chains, which outperforms existing approaches in our comparative analysis. Lastly, we validate the efficacy of our algorithm in real-world scenarios by deploying it on a robotic manipulator.

Online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four-fold obstacles: vendor lock-in, service availability, cost-ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses on three issues of Online Social Network (OSN) which include: (1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, (2) identifying the suitable datacenters that host the replicas according to latency time of different regions, and (3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods to adapt replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.

Autonomic IoT Application Placement in Edge/Fog Computing

Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of microservices that are mapped to edge computing and communication devices (fog nodes). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A Heuristic Solution Algorithm for a Comprehensive Optimal Phasor Measurement Unit Placement Considering Zero-Injection Buses and Practical Constraints in Power System State Observability Problem

A Heuristic Solution Algorithm for a Comprehensive Optimal Phasor Measurement Unit Placement Considering Zero-Injection Buses and Practical Constraints in Power System State Observability Problem

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

Reinforcement Learning Placement Algorithm for Optimization of UAV Network in Wireless Communication

Reinforcement Learning Placement Algorithm for Optimization of UAV Network in Wireless Communication

Setting Priorities in School Choice Enrollment Systems: Who Benefits from Placement Algorithm Preferences?

Setting Priorities in School Choice Enrollment Systems: Who Benefits from Placement Algorithm Preferences?

SUPPLY: Sustainable Multi-UAV Performance-Aware Placement Algorithm for Flying Networks

SUPPLY: Sustainable Multi-UAV Performance-Aware Placement Algorithm for Flying Networks

Sensor placement for data assimilation of turbulence models using eigenspace perturbations

We present an approach to sensor placement for turbulent mean flow data assimilation in the context of Reynolds-averaged Navier–Stokes (RANS) simulations. It entails generating a spatial uncertainty map through the eigenspace perturbations (ESPs) of the baseline turbulence model (e.g., the k−ω shear stress transport model) to quantify the epistemic structural errors in the model. A novel greedy search algorithm is proposed to place sensors targeting regions of highest uncertainty in the spatial uncertainty map generated from ESP. The algorithm is computationally efficient (e.g., computational cost negligible compared to a RANS solution) and is both easy to implement and tune. It involves two hyper-parameters (a constraint to avoid sensor clustering and the number of sensors) which we investigate in-depth. A variational (adjoint-based) data assimilation approach is used for flow reconstruction. The proposed strategy was tested on three two-dimensional wall-bounded flows (Reynolds number ranging 5.6×103–9.36×105) involving flow separation and reattachment. For the wall-mounted hump case, we found that data assimilation using 33 sensors with our proposed sensor placement algorithm reduced the average velocity prediction error by 60% vs 38% with a simple uniform placement of sensors. Furthermore, we found that we could achieve 61% error reduction using our algorithm with only three sensors. Notably, in all tested cases, the error reduction using our method for sensor placement was close in accuracy to the instances where the entire flow field data were used for flow reconstruction, which involved two to three orders of magnitude more data points than the placed sensors.

Improving mechanical performance in material extrusion parts via optimized toolpath planning

The material extrusion (MEX) additive manufacturing (AM) process is often overlooked for producing parts in load-bearing applications due to poor inter- and intra-layer bonds. These bonds are weak relative to the deposition direction, creating an overall anisotropic mechanical performance. However, this anisotropy presents an opportunity to tune part performance through the toolpath. Especially with a composite material, changing the deposition direction to preferentially align mechanical anisotropy with load paths could provide a route to increased performance. In a more general sense, any set of design objectives could be used to generate an orientation field (e.g., load paths for strength, routes for thermal conductivity, etc.) to control alignment of deposition directions.In combination with the inherent geometric flexibility of AM, there is an opportunity to simultaneously optimize part topology and its printing toolpath. As a step towards this goal, this paper presents a toolpath planning algorithm, based on streamline placement algorithms used in computational fluid dynamics, for generating optimized toolpaths where every deposition is aligned to an arbitrary orientation field. In particular, the algorithm is designed to continuously connect a discrete orientation field to improve deposition (and therefore composite reinforcement) length. The presented algorithm is then compared to more typical MEX infill patterns using topology- and toolpath orientation-optimized structures undergoing both tension and three-point bending loads. The toolpath planning strategies are evaluated in terms of (1) alignment to the orientation field and (2) mechanical performance. Relative to more conventional toolpath planning methods, the presented algorithm produces toolpaths with better alignment to the optimized orientation fields (>97%), and the printed parts demonstrate increased mechanical efficiencies.

Optimal 2D Placement of Virtual Objects in Physical Space for Augmented Reality Applications

Optimal 2D Placement of Virtual Objects in Physical Space for Augmented Reality Applications

Adaptive Streaming Transmission Optimization Method Based on Three-Dimensional Caching Architecture and Environment Awareness in High-Speed Rail

In high-mobility scenarios, a user’s media experience is severely constrained by the difficulty of network channel prediction, the instability of network quality, and other problems caused by the user’s fast movement, frequent base station handovers, the Doppler effect, etc. To this end, this paper proposes a video adaptive transmission architecture based on three-dimensional caching. In the temporal dimension, video data are cached to different base stations, and in the spatial dimension video data are cached to base stations, high-speed trains, and clients, thus constructing a multilevel caching architecture based on spatio-temporal attributes. Then, this paper mathematically models the media stream transmission process and summarizes the optimization problems that need to be solved. To solve the optimization problem, this paper proposes three optimization algorithms, namely, the placement algorithm based on three-dimensional caching, the video content selection algorithm for caching, and the bitrate selection algorithm. Finally, this paper builds a simulation system, which shows that the scheme proposed in this paper is more suitable for high-speed mobile networks, with better and more stable performance.

Reconfigurable Converter Topologies for EV Fast Charging Stations

Infrastructure for charging electric vehicles (EVs) is highly demanded due to the rising number of EVs on the road. Stations for charging electric vehicles are necessary for the ongoing transportation e-mobility. In particular, fast charging infrastructures increase the computing ability of transmission grids that are already under a lot of pressure. The market's current energizing foundation takes a ton of room and incidentally causes gridlocks, which raises the risk of mishaps and hinders crisis vehicles. The cost of installing this charging infrastructure increases significantly because the current system needs a lot of room. To resolve these issues, this work depicts a Reconfigurable Multidevice Interleaved Boost Converter Topologies with Placement Algorithm for adjusting and a space-proficient charging foundation. Because of its insignificant electromagnetic impedance, low input and result voltage swells, bidirectionality, high efficiency and dependability, delicate switching, commotion free activity, low exchanging loss, and high effectiveness, this topology is the most ideal decision for high-power EVs.