A thermal stress loading technique for large-sized hot dry rock mechanical tests

Testing of large-sized specimens is becoming increasingly important in deep underground rock mechanics and engineering.In traditional mechanical loading,stresses on large-sized specimens are achieved by large host frames and hydraulic pumps,which could lead to great investment.Low-cost testing machines clearly always have great appeal.In this study,a new approach is proposed using thermal expansion stress to load rock specimens,which may be particularly suitable for tests of deep hot dry rock with high temperatures.This is a different technical route from traditional mechanical loading through hydraulic pressure.For the rock mechanics test system of hot dry rock that already has an investment in heating systems,this technology may reduce the cost of the loading subsystem by fully utilizing the temperature changes.This paper presents the basic principle and a typical design of this technical solution.Preliminary feasibility analysis is then conducted based on numerical simulations.Although some technical details still need to be resolved,the feasibility of this loading approach has been preliminarily confirmed.

Distribution and exploration direction of medium-and large-sized marine carbonate gas fields in Sichuan Basin, SW China

Based on the analysis of the basic characteristics of medium-and large-sized marine gas fields in Sichuan Basin, combined with the division of major reservoir forming geological units in the marine craton stage and their control on key hydrocarbon accumulation factors, the distribution law of medium-and large-sized marine carbonate gas fields in the basin was examined and the exploration direction was pointed out. Through the analysis of the periodic stretching-uplifting background, it is concluded that five large scale paleo-rifts, three large scale paleo-uplifts, five large scale paleo erosion surfaces were formed in the marine craton stage of Sichuan Basin, and these geological units control the key reservoir forming factors of medium and large sized gas fields:(1) Large-scale paleo-rifts control the distribution of high-quality hydrocarbon generation centers.(2) The margin of large-scale paleo-rifts, high position of paleo-uplifts and paleo erosion surfaces control the distribution of high-quality reservoirs.(3) Large-scale paleo-rifts, paleo-uplifts, paleo erosion surfaces and present tectonic setting jointly control the formation of many types of large and medium-sized traps.(4) Natural gas accumulation is controlled by the inheritance evolution of traps in large geological units. Based on the comparative analysis of the distribution characteristics of medium-and large-sized gas fields and large geological units, it is proposed that the superimposition relationship between single or multiple geological units and the present structure controls the distribution of medium-and large-sized gas fields, and the "three paleo" superimposed area is the most advantageous. According to the above rules, the main exploration fields and directions of medium-and large-sized marine carbonate gas fields in Sichuan Basin include periphery of Deyang-Anyue paleo-rift, eastern margin of Longmenshan paleo-rift, margins of Kaijiang-Liangping oceanic trough and Chengkou-western Hubei oceanic trough, the high part of the subaqueous paleo-uplifts around Central Sichuan, paleo erosion surfaces of the top boundary of Maokou Formation in eastern and southern Sichuan Basin, paleo erosion surfaces of the top boundary of the Leikoupo Formation in central and western Sichuan Basin.

Hierarchical Algorithms of Large-Scale Systems with Nonseparable Multiobjective

A class of large-scale hierarchical control systems is considered, the overall objective function is a nonlinear and nonseparable function of multiple quadratic performance indices.The separation strategy of the multiobjective optimization technique and the three-level objective coordination method are applied to the large -sacle systems, and a four-level hierarchical algorithms of optimization control is obtained.

MCMOD:The Multi-Category Large-Scale Dataset for Maritime Object Detection

The marine environment is becoming increasingly complex due tothe various marine vehicles,and the diversity of maritime objects poses a challengeto marine environmental governance.Maritime object detection technologyplays an important role in this segment.In the field of computer vision,there is no sufficiently comprehensive public dataset for maritime objects inthe contrast to the automotive application domain.The existing maritimedatasets either have no bounding boxes(which are made for object classification)or cover limited varieties of maritime objects.To fulfil the vacancy,this paper proposed the Multi-Category Large-Scale Dataset for MaritimeObject Detection(MCMOD)which is collected by 3 onshore video camerasthat capture data under various environmental conditions such as fog,rain,evening,etc.The whole dataset consists of 16,166 labelled images alongwith 98,590 maritime objects which are classified into 10 classes.Comparedwith the existing maritime datasets,MCMOD contains a relatively balancedquantity of objects of different sizes(in the view).To evaluate MCMOD,this paper applied several state-of-the-art object detection approaches fromcomputer vision research on it and compared their performances.Moreover,a comparison between MCMOD and an existing maritime dataset was conducted.Experimental results indicate that the proposed dataset classifies moretypes of maritime objects and covers more small-scale objects,which canfacilitate the trained detectors to recognize more types of maritime objects anddetect maritime objects over a relatively long distance.The obtained resultsalso showthat the adopted approaches need to be further improved to enhancetheir capabilities in the maritime domain.

Modeling and simulation of 3D thermal stresses of large-sized castings in solidification processes

When heavy machines and large scaled receiver system of communication equipment are manufactured, it always needs to produce large-sized steel castings, aluminum castings and etc. Some defects of hot cracking by thermal stress often appear during solidification process as these castings are produced, which results in failure of castings. Therefore predicting the effects of technological parameters for production of castings on the thermal stress during solidification process becomes an important means. In this paper, the mathematical models have been established and numerical calculation of temperature fields by using finite difference method (FDM) and then thermal stress fields by using finite element method (FEM) during solidification process of castings have been carried out. The technological parameters of production have been optimized by the results of calculation and the defects of hot cracking have been eliminated. Modeling and simulation of 3D thermal stress during solidification processes of large-sized castings provided a scientific basis, which promoted further development of advanced manufacturing technique.

Rolling Fatigue Test of Large-Sized UHPC Member for Cable Stayed Bridge

Recently, research strives to apply Ultra High Performance Concrete (UHPC) to large-sized structures owing to its remarkable mechanical performance and durability compared to normal concrete. The Korea Institute of Construction Technology proposed SuperBridge800, an edge girder type UHPC cable stayed bridge with central span of 800 m, through its detailed design. The bridge is designed to be erected through the connection of precast UHPC segments. The precast UHPC segment is monolithically composed of one ribbed deck slab and edge girders at each side. The connection between the precast segments is achieved by steel bars at the edge girders and by UHPC cast-in-place wet joint at the slab. Despite of the outstanding mechanical performance of UHPC, the fabrication of large-sized members is a difficult task since UHPC hardens faster than normal concrete and requires a special curing process. Therefore, the constructability of large-sized UHPC segment should be secured to achieve SuperBridge800. Besides, the performance of the connection between segments should also be guaranteed, especially in terms of the fatigue performance of the UHPC cast-in-place joint, which constitutes a weak point. To that goal, two half-scaled UHPC segments are manufactured and the constructability is examined by fabricating a large-sized UHPC member connected with respect to the design conditions. This study conducts rolling fatigue test on the so-fabricated large-sized UHPC member. Rolling fatigue test is carried out up to 2 million cycles considering actual vehicle load at each center and quarter points of the member. The test results confirm that the service limit state is satisfied.

Experimental investigation and numerical simulation of large-sized aluminum tube extrusion forming

Large-sized aluminum tube has big section effect, aspect ratio and thin thickness, so that the extrusion technology is complex and the large specific pressure is generated in extrusion cavity. The temperature variation and velocity effect is difficult to control. The extrusion forming of large-sized aluminum tube was researched and simulated. Three-dimensional thermo-mechanical coupled finite element model was constructed and appropriate boundary conditions were given out. The results show that large-sized aluminum tube can be formed by isothermal extrusion through controlling the extrusion velocity and founding the relationship between extrusion velocity and extrusion temperature.

A Study of Precision Factors of Large-scale Object Surface Profile Laser Scanning Measurement

In this paper, we presented a method of using the l as er scanning triangulation for the non-contact 3D surface profile measurement of large-scale object. The characteristic of large-scale object non-contact mea surement is analyzed and the measuring method is proposed. Main factors influenc ing measurement precision such as image distortion and accurate designation of s peckle center are analyzed and methods of solving these problems are proposed. W e designed a combined filter by which the pulse noise and the Gaussian noise of speckle image can be eliminated efficiently. Using the characteristic of intensi ty distribution of laser speckle image we proposed a new approximating method th at could locate the center of laser speckle image at sub-pixel. The auxiliary v ariables are set to linearize the relationship between the image displacement an d the distance, the accurate values of laser triangulation system parameters cou ld be calibrated accurately and the measuring precision is increased remarkabl y. Using the above techniques we designed a measuring system based on laser sc anning triangulation. The results of the experiment show that these methods can raise the measuring precision of large-scale 3D surface profile effectively.

A method of manufacturing process modeling based on activity network for large-sized and complex products

Simulation technique is an efficient approach to realize the planning and scheduling of manufacturing process of products. An appropriate and efficient manufacturing process model is the basis and key of manufacturing process simulation. By analyzing the features of large-sized and complex products, a method of manufacturing process modeling based on activity network is presented and a mapping algorithm of translating BOM/BOP into the manufacturing process model is designed in detail.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Large-Scale Distribution of Herbig-Haro Objects in Taurus

We report our new results on Herbig-Haro (HH) objects in the star forming region of Taurus from a wide-field survey with the 60/90 cm Schmidt telescope of the Beijing Astronomical Observatory. This survey using CCD imaging with a narrow band [SII] filter and an intermediate band [BATC10] filter covered approximately 30 square degrees in Taurus. Besides confirming the known HH Objects in the region, we discovered seven new HH candidates, and groups. Six of these are HH 701A-B, HH 702A-D, HH 703, HH 704A-D, HH 705, HH 706 and the seventh is a group, a new component of HH 319, labeled HH 319B-D. Based on the large-scale distribution of pre-main-sequence (PMS) stars in Taurus, we analyze statistically the most probable distance from the HH objects to each PMS star, from which we estimate the typical timescale of these HH objects to be between (1.3?2.0)×104 yrs; and we also obtain the birth rates of HH objects: 0.447±0.198 for Class I PMS stars, 0.360±0.222 for Class II PMS stars, and ?0.148±0.234 for Class III PMS stars.

Masked Autoencoders as Single Object Tracking Learners

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

Weak-focused acoustic vortex generated by a focused ring array of planar transducers and its application in large-scale rotational object manipulation

Contactless manipulation of multi-scale objects using the acoustic vortex(AV) tweezers offers tremendous perspectives in biomedical applications.However,it is still hindered by the weak acoustic radiation force(ARF) and torque(ART)around the vortex center.By introducing the elevation angle to the planar transducers of an N-element ring array,the weakfocused acoustic vortex(WFAV) composed of a main-AV and N paraxial-AVs is constructed to conduct a large-scale object manipulation.Different from the traditional focused AV(FAV) generated by a ring array of concave spherical transducers,a much larger focal region of the WFAV is generated by the main lobes of the planar transducers with the size inversely associated with the elevation angle.With the pressure simulation of the acoustic field,the capability of the rotational object driving in the focal plane for the WFAV is analyzed using the ARF and the ART exerted on an elastic ball based on acoustic scattering.With the experimental system built in water,the generation of the WFAV is verified by the scanning measurements of the acoustic field and the capability of object manipulation is also analyzed by the rotational trapping of floating particles in the focal plane.The favorable results demonstrate the feasibility of large-scale rotational manipulation of objects with a strengthened ART and a reduced acousto-thermal damage to biological tissues,showing a promising prospect for potential applications in clinical practice.

Application of 3D Scanned Big Data of Large-scale Cultural Heritage Objects Based on Noise-robust Transparent Visualization

Three-dimensional(3D) scanning technology has undergone remarkable developments in recent years.Data acquired by 3D scanning have the form of 3D point clouds.The 3D scanned point clouds have data sizes that can be considered big data.They also contain measurement noise inherent in measurement data.These properties of 3D scanned point clouds make many traditional CG/visualization techniques difficult.This paper reviewed our recent achievements in developing varieties of high-quality visualizations suitable for the visual analysis of 3D scanned point clouds.We demonstrated the effectiveness of the method by applying the visualizations to various cultural heritage objects.The main visualization targets used in this paper are the floats in the Gion Festival in Kyoto(the float parade is on the UNESCO Intangible Cultural Heritage List) and Borobudur Temple in Indonesia(a UNESCO World Heritage Site).

A Large-Scale Survey for Extremely Cold Young Stellar Objects in the Galaxy

We conducted a large-scale survey of the extremely-cold infrared sources(ECISs) along the Galactic plane. There are 1912 (IRAS) sources selected on the basis of their color indices and their association with recent star formation. A quick survey was made toward 724 sources. There are 251 sources detected with significant CO emission during the quick survey above the detection limit of 0 9 K. Among the various sources detected, there are 147 sources found to have broad CO wing emission, including 116 newly detected sources. These sources comprise a new database for future study of star formation in our Galaxy. Using the known outflow sources as an effective indicator, we found the outflow detection rate of the quick survey is 62%, reasonably sensitive in survey for new outflow sources. Results from limited follow-up studies are introduced.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

Confusing Object Detection:A Survey

Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.

Floating Waste Discovery by Request via Object-Centric Learning

Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.