Position In Frame Research Articles

A Two-Stage Optimisation of Ship Hull Structure Combining Fractional Factorial Design Technique and NSGA-II Algorithm

The intricate nature of ships and floating structures presents a significant challenge for ship designers when determining suitable structural dimensions for maritime applications. This study addresses a critical research gap by focusing on a three-cargo hold model for a multipurpose cargo ship. The complex composition of these structures, including stiffening plates, deck plates, bottom plates, frames, and bulkheads, necessitates thorough structural analysis to facilitate effective and cost-efficient design evaluation. To address this challenge, the research utilises FEMAP-integrated NX NASTRAN software (2021.2) to assess hull girder stress. Furthermore, a novel approach is introduced, integrating the Design of Experiments (DOE) principles within Minitab 21.4.1 software to identify critical parameters affecting hull girder stress and production costs. This method determined the top five key parameters influencing hull girder stress: Hatch coaming plate, Hatch coaming top plate, Main deck plate, Shear strake plate, and Bottom plate, while also highlighting key parameters that impact production costs: the inner bottom plate, Inner side shell plate, Bottom plate, Web frame spacing, and Side shell plate. Ship design optimisation is then carried out by incorporating regression equations from Minitab software into the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is managed using Python software (PyCharm Community Editon 2020.3.1). This optimisation process yields a significant 10% reduction in both ship weight and production costs compared to the previous design, achieved through prudent adjustments in plate thickness, web frame positioning, and stiffener arrangement. The optimally designed midship section undergoes rigorous validation to ensure conformity with industry standards and classification society regulations. Necessary adjustments to inner bottom plates and double bottom side girders are made to meet these stringent requirements. This research offers a comprehensive framework for the structural optimisation of ship hulls, potentially enhancing safety, sustainability, and competitiveness within the maritime engineering industry.

Online calibration method for SINS/LDV integrated navigation system based on left group error definition

The integration of strapdown inertial navigation system (SINS) and laser Doppler velocimeter (LDV) is a reliable technology for land vehicle positioning. To ensure the best positioning performance of the SINS/LDV integrated navigation system, it is necessary to calibrate it accurately. However, the accuracy of the error model of the traditional calibration method is seriously affected by the large misalignment angle, which in turn affects the accuracy and consistency of the filtering, and eventually leads to the decline of the calibration accuracy. Therefore, this paper introduces the Lie group theory for the first time into the calibration study of the SINS/LDV integrated navigation system. Based on the error state vector defined by the left group error definition in the Lie group, the three calibration models of the SINS/LDV integrated navigation system are derived in the Earth-centered Earth-fixed frame, using velocity, displacement increment, and dead reckoning (DR) position, which are the three common observation information. The most significant advantage of these calibration models is their ability to handle large initial misalignment angles. The calibration models proposed in this paper are comprehensively evaluated by two long-distance vehicle experiments. The test results show that under normal conditions (no large attitude misalignment angle and all sensors are working properly), the Lie group-based calibration methods have similar performance to the traditional calibration method, but they have significant advantages in the case of large initial attitude deviation. In addition, using displacement increment and DR position as observations improves calibration performance compared to velocity.

Spatial–temporal feature-based End-to-end Fourier network for 3D sign language recognition

Most dynamic sign word misclassifications are caused by redundant spatial–temporal (SPT) feature pruning that lacks language semantic and temporal dependencies. SPT feature recognition is one of the important aspects for the evaluation of the misclassification of dynamic sign words. The redundant pruning of SPT feature space influences the language model of sign confusion, model complexity, and SPT feature similarity. The purpose of this article is to develop a new multi-scale SPT feature-based dynamic sign word recognition approach via a low-cost feature selection method (FS) and End-to-end Fourier convolution neural network (EFCNN). Instead of a sensor fusion technique for obtaining frame position alignment, in the EFCNN, new 3D frame position and coordinates are determined using a pixel weighting and alignment function of the first and succeeding 25 spatial intensities of the 3D video changes across hand motion. The new spatial weight and the original spatial coordinates are fused and truncated in the Fourier domain. We generate the temporal dependence of the fused features. A feature selection known as the FS-EFCNN is introduced to select compact features with a preserved language meaning. Five state-of-the-art feature selection methods, namely Infinite FS (InFS), Relief FS, Fisher, MIM, ILFS, and ensemble FS-EFCNN were deployed to guide and optimize the learning performance of EFCNN. The experimental result analysis highlighted the improved results of the FS-EFCNN method with the best accuracy of 99.86%, 99.89%, and 90.69% on 3D American Sign Language, British Sign Language, and Greek Sign Language data sets, respectively.

Playing Checkers with an Intelligent and Collaborative Robotic System

Collaborative robotics represents a modern and efficient framework in which machines can safely interact with humans. Coupled with artificial intelligence (AI) systems, collaborative robots can solve problems that require a certain degree of intelligence not only in industry but also in the entertainment and educational fields. Board games like chess or checkers are a good example. When playing these games, a robotic system has to recognize the board and pieces and estimate their position in the robot reference frame, decide autonomously which is the best move to make (respecting the game rules), and physically execute it. In this paper, an intelligent and collaborative robotic system is presented to play Italian checkers. The system is able to acquire the game state using a camera, select the best move among all the possible ones through a decision-making algorithm, and physically manipulate the game pieces on the board, performing pick-and-place operations. Minimum-time trajectories are optimized online for each pick-and-place operation of the robot so as to make the game more fluent and interactive while meeting the kinematic constraints of the manipulator. The developed system is tested in a real-world setup using a Franka Emika arm with seven degrees of freedom. The experimental results demonstrate the feasibility and performance of the proposed approach.

Enhancing medical image object detection with collaborative multi-agent deep Q-networks and multi-scale representation

Object detection holds a crucial role in medical diagnostics. Tasks like organ segmentation and malignancy diagnosis typically necessitate preliminary localization of corresponding anatomical structures. Precise positioning ensures that only pertinent regions require processing, leading to a potential reduction in computational and storage demands. Conventional image detection approaches necessitate numerous candidate boxes, resulting in redundant computations. Developing techniques capable of accurately detecting medical image objects without reliance on candidate boxes holds substantial practical significance. This paper introduces a 2D method for detecting medical image objects, which leverages multi-agent deep Q-network reinforcement learning and a multi-scale image representation. The method constructs a collaborative environment for multiple agents. These agents individually govern the upper-right corner and lower-left corner positions of the object detection frame, progressively converging toward the actual endpoint through iterative interactions. To expedite the detection process, a multi-scale image representation technique is employed. This method segments the process into three scales. Initially, within the coarse-scale space, the agent approximates the region containing the true endpoint, subsequently executing oscillatory movements. Progressively, it refines its approach within the fine-scale space, advancing toward the genuine endpoint with smaller iterative steps. The detection results demonstrate that collaborative detection among agents yields a 2.45%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} higher intersection over union compared to non-collaborative detection. Agents exhibit varying step sizes and fields of view in different scale spaces, leading to a reduction in detection time by 0.12 s compared to single-scale comparison. Experimental outcomes demonstrate the superiority of the medical image target detection method proposed in this study over prevailing mainstream detection algorithms.

Detection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent size

Autonomous driving requires information such as the velocity of other vehicles to prevent potential hazards. This work proposes a real-time deep learning-based framework to estimate vehicle speeds from image captures through an onboard camera. Vehicles are detected and tracked by the proposed deep neural networks and a tracking algorithm, which analyzes the trajectories. Finally, a linear regression model estimates the speed of a vehicle based on its position and size in the camera frame. This proposal has been tested with two sequences of the Prevention dataset with satisfactory results. The system can estimate the speed of multiple vehicles simultaneously. It can be integrated easily with onboard computer systems, thus allowing to development of a low-cost solution for speed estimation in an everyday vehicle. The potential applications include vehicle safety systems, driver assistance, and autonomous driving technologies.

Constraining effect of load frames during full-scale rotor blade fatigue testing

In rotor blade fatigue tests used for the certification process, load frames are used to introduce the loads to the blade. These load frames have a constraining effect on the cross-sectional deformation. This work investigates these constraining effects using a finite element contact analysis on a current-generation commercial rotor blade. Two different load frame variants are considered: a conventional load frame covering most of the cross-section and a reduced load frame covering only the main spar caps. The interaction between blade and load frame is implemented via contact formulations, which allows pretensioning in combination with a flap and a lead-lag load case. Strains on the outer surface of the blade are evaluated and compared to an artificial reference loading. The area of influence of the load application through the load frame, in which the strains deviate significantly due to clamping effects, mostly corresponds to the 0.75 times the chord length assumed for the certification [1]. The strains in the trailing edge are significantly less affected by the reduced load frame variant than by the conventional one, thus potentially making it possible to consider the trailing edge at the position of the load frame as being properly tested for certification purposes.

Extended Kalman filters for close-range navigation to noncooperative targets

This work presents a set of dynamic filters for estimating the relative roto-translational state and the main parameters of a noncooperative target from an observing chaser satellite during close proximity operations. The proposed different options address a wide range of design possibilities for the architecture of the relative navigation system. All filters are derived from a common, general, core shaped as a dynamic multiplicative extended Kalman filter using dual quaternions. This allows exploiting the advantages of handling the pose (i.e., attitude and position) in a multiplicative fashion, while improving the accuracy in the estimation of the angular and linear relative velocities, as well as enabling the estimation of some meaningful parameters of the target spacecraft (e.g., the ratios of the moments of inertia, position and orientation of the principal axes frame). Moreover, by adopting relative kinematics and dynamics equations in dual quaternions, the inherent coupling of the six degrees-of-freedom motion is addressed with no approximations. All filters take as observations only the noisy pose measurements from an electro-optical device. For each proposed formulation, numerical simulations are carried out to show the behavior of the filter within a scenario representative of close-range target inspection at conclusion of the mid-range rendezvous.

A Dual-Modal Hybrid Gripper with Wide Tunable Contact Stiffness Range and High Compliance for Adaptive and Wide-Range Grasping Objects with Diverse Fragilities.

The difficulties of traditional rigid/soft grippers in meeting the increasing performance expectations (e.g., high grasping adaptability and wide graspable objects range) of a single robotic gripper have given birth to numerous soft-rigid coupling grippers with promising performance. However, it is still hard for these hybrid grippers to adaptively grasp various objects with diverse fragilities intact, such as incense ash and orange, due to their limited contact stiffness adjustable range and compliance. To solve these challenging issues, herein, we propose a dual-modal hybrid gripper, whose fingers contain a detachable elastomer-coated flexible sheet that is restrained by a moving frame as a teardrop shape. The gripper's two modes switched by controlling the moving frame position can selectively highlight the low contact stiffness and excellent compliance of the teardrop-shaped flexible sheets and the high contact stiffness of the moving frames. Moreover, the contact stiffness of the teardrop-shaped sheets can be wide-range adjusted by online controlling the moving frame position and offline replacing the sheets with different thicknesses. The compliance of the teardrop-shaped sheets also proves to be excellent compared with an Ecoflex 10 fingertip with the same profile. Such a gripper with wide-range tunable contact stiffness and high compliance demonstrates excellent grasping adaptability (e.g., it can safely grasp several fragile strawberries with a maximum size difference of 18 mm, a strawberry with a left/right offset of 3 cm, and a strawberry in two different lying poses) and wide-range graspable objects (from 0.1 g super fragile cigarette ashes to 5.1 kg dumbbell).

YH-Pose: Human pose estimation in complex coal mine scenarios

Human pose estimation in continuous video frames captured from complex coal mine scenes is challenging. The video frames in this scene may suffer from problems such as uneven brightness, blurred image details, and excessive noise. Mainstream pose estimation methods have good detection performance for high-quality static images, but their accuracy and prediction rate can be significantly reduced in coal mine scenes. In this work, a yielding human pose estimation framework is proposed, termed YH-Pose. The framework strives to incorporate additional visual evidences from neighboring frames to facilitate the pose estimation of the current frame. Firstly, a human detector is introduced to locate the person’s position in the video frame and extract global features. This can provide a good initialization for the latter keypoint detection, making the training process converge quickly. Secondly, heatmaps are used to encode the joint locations as Gaussian peaks, and a temporal road module (TRM) is designed, which encodes video frames at intervals. The module efficiently fuses spatio-temporal information through frame-rate groups in a hierarchical manner. Lastly, the spatial road module (SRM) learns from fused keypoint context features and predicts the location of the keypoints in the next frame. In addition, a dataset called Colliery-1 is proposed, which derives from underground surveillance video from chinese coal mines and consists of 3600 video clips. The experimental results on the Colliery-1 dataset indicate that the framework achieved an average accuracy of 82% and 80% on the training and test sets, respectively. Moreover, the framework achieved a 94.2% prediction rate for pose estimation. To further evaluate the effectiveness of the proposed method, some comparisons have been made between it and various mainstream methods using different metrics.

MGlif: A software tool for manual glottal inverse filtering

Despite decades of advances in automated estimation of the voice source (glottal flow) from the speech signal, manual glottal inverse filtering remains the gold standard; yet, options are limited regarding available software tools. Here, a new software application for glottal-flow estimation is introduced, with a graphical user interface designed for manual specification and tuning of inverse-filter parameters while results are updated and displayed in real-time. The user can flexibly control the signal polarity and sampling rate, the analysis frame position and duration, and the type of window and preemphasis for vocal-tract modeling. The tunable inverse-filter parameters are the frequencies and bandwidths of vocal-tract poles and zeros, and the lip-radiation coefficient. A single window displays graphical information in several panels: the speech waveform (scrollable), the selected frame, its log-power spectrum before and after preemphasis, the model spectrum including vocal-tract and lip-radiation effects, the estimated glottal-flow waveform, its derivative, and their log-power spectra. All information pertaining to an analysis may be saved at any time, ready to be reloaded for review or analysis resumption. Key data may also be exported for further use outside the application. The software will be made freely available to the research community.

Determination of Static Stresses in the Design of the Load-handling Frame of a Stationary Hoist

The article is devoted to the determination of static stresses in the design of the load-handling frame of a stationary hoist for loading bulk cargo into containers transported by railway platforms. Calculations have been carried out and digital models have been developed in software environments of applied simulation programs that will allow analyzing the efficiency of a stationary hoist. Mathematical modeling of static stresses in the design of the load-handling frame of the developed stationary hoist was performed when loading grain cargo into a container. The task of determining static stresses in the Mathcad software environment was set. Four test positions of the load-handling frame of a stationary hoist for loading bulk cargoes into containers transported by railway platforms are considered: І position – under loading; ІІ and ІІІ positions – when lowering the container; IV position – horizontal position of the container. The research was carried out within the framework of the implementation of the Republican grant No. AP14869550 SC MSHE RK. The values of maximum normal stresses in the design of the load-handling frame are obtained, which satisfy the strength condition

3D Position Estimation of Objects for Inventory Management Automation Using Drones

With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.

Characterization of swine hepatitis E virus genotype 3 open reading frames

The goal of this study was a determination of structural organization features of swine hepatitis E virus genotype 3 open reading frames. Gene selective pressure for genes was estimated using the Tajima’s neutrality test as implemented in MEGA 6. Shannon entropy was used for diversity detection at each position of open reading frames. Shannon entropy plots representing quantitative diversity at each amino acid (nucleotide) position of 11 swine HEV-3 isolates aligned sequences were calculated by software BioEdit. For phylogenetic analysis, nucleotide distances were analyzed using the neighbour-joining algorithm. Analysis of nucleotide, non-synonymous and synonymous distances was performed using MEGA6. Parameters of mutational analysis for swine hepatitis E virus genotype 3 open reading frames ORF1 – ORF3 were determined using Tajima’s neutrality test. Shannon entropy technique revealed that ORF3 is characterized by the most number of diverse amino acid residues (21,7 %) comparing with ORF1, ORF2 coding regions. Analysis of non-synonymous distances in ORF3 allowed to determine an order of evolutionary events in the history of swine HEV-3 isolates. Avian astroviruses strains were used as control because phylogenetic relationships among them correspond to the evolution history of their host species. Tree topology for swine HEV-3 ORF3 based on non-synonymous distances differs from tree structures based on nucleotide and synonymous distances. Positive values of D Tajima’s factor for ORF1, ORF2 of swine HEV-3 show on operating positive selection onto these coding regions. Negative value of D Tajima’s factor for ORF3 coding region indicates onto purifying selection

Relationships between the magnitude of representational momentum and the spatial and temporal anticipatory judgments of opponent's kicks in taekwondo.

For successful actions in a fast, dynamic environment such as sports, a quick successful anticipation of a forthcoming environmental state is essential. However, the perceptual mechanisms involved in successful anticipation are not fully understood. This study examined the relationships between the magnitude of representational momentum (RM) as a forward displacement of the memory representation of the final position of a moving object (which implies that observers perceptually "see" a near future forthcoming dynamic environmental state) and the temporal and spatial anticipatory judgments of the opponent's high or middle kicks in taekwondo. Twenty-seven participants (university taekwondo club members and non-members) observed video clips of taekwondo kicks that vanished at one of 10 frame positions prior to the kick impact and performed three tasks consecutively: anticipatory coincidence timing (CT) with the arrival of kick impact, judgment of the kick type (high and middle kicks) by forced choice, and judgment of the vanishing frame position (measuring RM). Our results showed significant group effects for the number of correct kick-type judgments and the judgment threshold for kick-type choice (kick-typeJT), which was estimated in terms of individual psychometric function curves. A significant correlation was found between the magnitude of RM (estimated at kick-typeJT) and kick-typeJT, but not between the CT errors (estimated at kick-typeJT) and kick-typeJT. This indicates that the magnitude of RM may play an influential role in quick kick-type judgments, but not in coincidence timing while observing an opponent's kick motion. These findings suggest that subjective anticipatory perception or judgment of the future spatial state is vital to anticipatory actions under severe time constraints.

Wordless Picturebooks as Resources for the Construction of the Pedagogy of Multiliteracies. The Case of Migrants, by Issa Watanabe

AbstractThis article presents the study of Migrants (2019), a wordless picturebook by Peruvian author Issa Watanabe, developed to answer the following research question: To what extent can this visual text become a resource for the learning of multiliteracies? A visual discourse analysis of the narrative was performed, focusing on the identification of key meanings and semiotic resources used in their visual construction. Our findings unveil the prominent role of semiotic resources such as anthropomorphism, vectors, colours, position and unboundedness of the page frame, in the visual realization of key meanings such as diversity, journeys, death, and empathy. Upon these findings, we present Migrants as an extraordinary literary text to initiate children in learning the visual mode, one of the multiple modes currently used in the representation of meaning. We discuss our findings with reference to the main question leading the study, the pedagogical conceptualizations endorsed by the multiliteracies theoretical framework and other relevant research. While the study suggests that wordless picturebooks are called to play a prominent role in the construction of the multiliteracies pedagogical agenda, it furthermore points to the potential of the pedagogy of multiliteracies in the conceptualization of literary pedagogy itself.

Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.

Fusing intraoperative 2-D ultrasound (US) frames with preoperative 3-D magnetic resonance (MR) images for guiding interventions has become the clinical gold standard in image-guided prostate cancer biopsy. However, developing an automatic image registration system for this application is challenging because of the modality gap between US/MR and the dimensionality gap between 2-D/3-D data. To overcome these challenges, we propose a novel US frame-to-volume registration (FVReg) pipeline to bridge the dimensionality gap between 2-D US frames and 3-D US volume. The developed pipeline is implemented using deep neural networks, which are fully automatic without requiring external tracking devices. The framework consists of three major components, including one) a frame-to-frame registration network (Frame2Frame) that estimates the current frame's 3-D spatial position based on previous video context, two) a frame-to-slice correction network (Frame2Slice) adjusting the estimated frame position using the 3-D US volumetric information, and three) a similarity filtering (SF) mechanism selecting the frame with the highest image similarity with the query frame. We validated our method on a clinical dataset with 618 subjects and tested its potential on real-time 2-D-US to 3-D-MR fusion navigation tasks. The proposed FVReg achieved an average target navigation error of 1.93 mm at 5-14 fps. Our source code is publicly available at https://github.com/DIAL-RPI/Frame-to-Volume-Registration.

Enhancing Lane-Tracking Performance in Challenging Driving Environments through Parameter Optimization and a Restriction System

The autonomous driving market has experienced rapid growth in recent times. From systems that assist drivers in keeping within their lanes to systems that recognize obstacles using sensors and then handle those obstacles, there are various types of systems in autonomous driving. The sensors used in autonomous driving systems include infrared detection devices, lidar, ultrasonic sensors, and cameras. Among these sensors, cameras are widely used. This paper proposes a method for stable lane detection from images captured by camera sensors in diverse environments. First, the system utilizes a bilateral filter and multiscale retinex (MSR) with experimentally optimized set parameters to suppress image noise while increasing contrast. Subsequently, the Canny edge detector is employed to detect the edges of the lane candidates, followed by utilizing the Hough transform to make straight lines from the land candidate images. Then, using a proposed restriction system, only the two lines that the current vehicle is actively driving within are detected from the candidate lines. Furthermore, the lane position information from the previous frame is combined with the lane information from the current frame to correct the current lane position. The Kalman filter is then used to predict the lane position in the next frame. The proposed lane-detection method was evaluated in various scenarios, including rainy conditions, low-light nighttime environments with minimal street lighting, scenarios with interfering guidelines within the lane area, and scenarios with significant noise caused by water droplets on the camera. Both qualitative and quantitative experimental results demonstrate that the lane-detection method presented in this paper effectively suppresses noise and accurately detects the two active lanes during driving.

Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning.

In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle's distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates.

Energy cost of localization of relational quantum information

Entanglement of spatially separated quantum states is usually defined with respect to a reference frame provided by some external observer. Thus, if one wishes to localize the quantum information within a spatially separated entangled state, one must enact an entanglement extraction protocol also defined with respect to that external frame. Entanglement extraction for Gaussian ground states in such an external frame construction has been shown to require a minimum energy and is hence an interesting process for gravitational physics, where examinations of localization vs energy cost have a long history. General covariance, however, precludes dependence on external frames. In order to enact an extraction protocol in a generally covariant theory, dependence on the external reference frame must first be removed and the states made relational. We examine the implementation of an extraction protocol for Gaussian states, whose center of mass and relational degrees of freedom are entangled, in a relational toy model where translation invariance stands in for full diffeomorphism invariance. Constructing fully relational states and the corresponding extraction/localization can, in principle, be done in two ways. External frame position information can be removed through $G$-twirling over translations or one can spontaneously break the translation symmetry via the gradient of an auxiliary field, or $Z$-model. We determine the energetics of quantum information localization after the states have been made fully relational via both the $G$-twirl and $Z$-model. We also show one can obtain the $G$-twirl construction from a $Z$-model as a limit of positive operator valued measurements.