Two-level Structure Research Articles

Value of Virtual Reality Technology in Image Inspection and 3D Geometric Modeling

Aiming at the poor expressive ability of image statistical information during the reconstruction process of traditional 3D image reconstruction method based on virtual reality technology, resulting in low accuracy of 3D image after reconstruction, a new image detection and 3D image reconstruction based on virtual reality technology are studied method. This paper first proposed a new two-level cascade convolutional neural network structure. The first level of the network predicts target positioning based on the image-level labels of the training image, generates a bounding box of the target in the original image, and generates a cropped image. The cropped image is input to the second-level network. The cropped image may contain areas where the target is stuck in the original image. Level 2 networks only use the adhesion area as training data. Secondly, the visualization software development platform and virtual reality 3D image processing software are selected as the platform for 3D image reconstruction. After the original image is imported into the computer through data input and file analysis steps, the original image is detected. The detected image is in the virtual in the real software, the bounding box method is first used to construct the three-dimensional data field of image reconstruction, and the three-dimensional direct volume of the image is drawn according to the three-dimensional data field of image reconstruction. Preferably, the three-dimensional image reconstruction output formula is obtained through the three-dimensional image direct volume to realize the three-dimensional image reconstruction based on the virtual reality technology. The simulation results show that the method proposed in this paper can effectively detect images. The average traversal coverage of 3D image reconstruction is up to 0.979, and the reconstruction accuracy is higher than 0.97.

An IoT-oriented real-time storage mechanism for massive small files based on Swift

In the internet of things (IoT), large amounts of small files are generated from various structure sensors in cloud storage platforms. Real-time storage of massive small files will put great pressures to the traditional file system. By studying the real-time storage strategy of massive small files based on the object storage architecture Swift, we proved a unique aggregation storage strategy, called sequential data aggregation strategy (SDAS), for storage of small files. We designed a two-level index structure to improve writing rate by transferring randomly write to sequentially write. To improve overall data access efficiency and solve the performance bottleneck of proxy node, we utilise a file's potential relevance of timing to prefetch related files that are merged in other blocks. Simulation results show that relative to the original system, SDAS has shorter response time of writing operation, lower cost of index maintenance cost, more balanced node load and 30% reduction in disk overhead.

СПОСОБ ФОРМИРОВАНИЯ ДЕСКРИПТИВНОЙ МОДЕЛИ ПРОЦЕССА ОПЕРАТИВНОГО ВОССТАНОВЛЕНИЯ ИЗДЕЛИЙ ВВТ ГРУППИРОВКИ ПВО НА ОСНОВЕ ОНТОЛОГИЙ

The operative restoration process of weapons and military equipment of the air defense group is considered from the point of view of its model representation, intended for the automation of the duty personnel activities of the situational management of repair and maintenance center. In this paper, the reduction in the time required to generate operational information is associated with the improvement of automation tools for information support of the process under consideration by switching to formalized models that belong to the descriptive class. Ontologies are used as a mathematical tool for creating such models. Despite the use of ontologies for model representation of various subject areas, there is no way to construct a descriptive model of the presented process using the mathematical apparatus of ontologies. We propose a two-level structure of the descriptive model of this process in the form of a set of subject and applied ontologies. We consider the procedures that ensure the formation of a unified ontology of the process under consideration. The peculiarity of using the descriptive model is to allow the personnel of the duty shift of the situational management center to use natural language terms to form the requests for obtaining the necessary operational information. This helps to increase the information content of requests from the personnel of the duty shift of the situational management center. The use of a descriptive model in automation tools is aimed at reducing the time of the operative restoration of weapons and military equipment of the air defense group by reducing the time of formation of operational information necessary for the implementation of the activities of the personnel of the duty shift of the situational management center.

Magnetopolaron-state lifetime and qubit decoherence in donor-center quantum dots with the electromagnetic field

Recently, the measurement scheme of quantum dot qubit decocoherence quantized by the longitudinal optical (LO) phonon spontaneous emission rate has attracted the attention and discussion of many researchers. However, it is not difficult to see that the above-mentioned measurement scheme still has some insufficient and imperfect aspects that are to be studied urgently. Considering from the physical mechanism, the essence of the above scheme is to quantify the decoherence time of qubit by using the excited state decay time or excited state lifetime of the polaron. However, so far, there is little research on how the ground state decay time and ground state lifetime of two-state polaron affect the coherence of qubit. There is no doubt that this is an equally important research topic. This is because, firstly, for the coherence of the quantum state of polaron, both the decay of the excited state and the decay of the ground state will destroy or attenuate the qubit coherence, secondly, the transition rate of the two-state polaron from the ground state to the excited state after absorbing an LO phonon is also a function quantifying the qubit decoherence time of two-state system of which the inverse is called the ground state decay time or the ground state lifetime. It may be called a measure of qubit decoherence time quantized by the ground state decay time or ground state lifetime of polaron. In this article, the ground-state and excited-state energy and wave function of the magnetopolaron in a donor-center quantum dot with asymmetric Gaussian potential are derived by Lee-Low-Pines transformation and Pekar-type variational methodd, and then the two-level structure for a qubit is constructed. The measure of qubit decoherence time of quantum dots quantified by ground state decay time of two-state polaron is established, which is compared with the well-known measure of qubit decoherence time of quantum dots quantified by polaron excited state decay time, and their physical mechanisms are revealed. By studying the influence of dielectric constant ratio, electro-phonons coupling constant, temperature and electromagnetic field on the ground state lifetime of magnetopolaron in the donor-center quantum dots with asymmetric Gaussian potential, the influences of material properties, temperature, electromagnetic field and other environmental factors on qubit decoherence of quantum dots are revealed, thereby revealing the mechanism of qubit decoherence caused by LO phonon effect.

ГЕОЛОГО-ГЕОМОРФОЛОГИЧЕСКИЕ УСЛОВИЯ ФОРМИРОВАНИЯ И СОХРАННОСТИ РОССЫПЕЙ ЗОЛОТА В БЕЛЬСКОЙ МЕЖГОРНОЙ ДЕПРЕССИИ (ЮЖНЫЙ УРАЛ)

An analysis of the geological and geomorphological features of the formation and preservation of gold placers in the Pribelsky placer zone located in the central part of the Belsky intermountain depression in the Southern Urals revealed the following. Valley of River Belay within Belsky depression, inheriting a zone of the Zyuratkulsky deep break, has complex two-level structure. The upper tier is represented by a wide (up to 5 km) trough-like paleovalley with complexes of polygenic and polychronic Miocene-Pliocene formations, both preserved in deep (up to 20–37 m) erosion-karst depressions and in some places coming to the surface. In Beloretsky and Burzyansky prospective sites of these complexes gold-bearing was revealed. As a result of the Eopleistocene – Early Neopleistocene phase of the neo-tectonic uplift of the mountainous part of the Bashkir Urals and subsequent river incision into the bottom of the paleo-valley to a depth of 30 to 130 m, a complex of sediments of the lower tier of the modern Belay river valley was formed. Due to the incision and reworking of goldbearing formations of the upper tier, the weight contents of gold are also established in the braid deposits of the river Belay. It has been shown that the areas of paleo-valley river Belay with karst troughs-traps of placer gold associated with zones of increased fracturing and permeability at the junctions of the submeridional and secant transverse faults. Attention is also drawn to the sites of the inheritance of the paleo-valley by a modern network with a predicted greater concentration of metal in them, even if weakly gold-bearing loose formations.

Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes

The Gaussian graphical model has been a popular tool for investigating the conditional dependency structure between random variables by estimating sparse precision matrices. The estimated precision matrices can be mapped into networks for visualization. However, the ability to investigate the conditional dependency structure when a two-level structure exists among the variables is still limited. Some variables are considered as higher-level variables while others are nested in these higher-level variables—the latter are called lower-level variables. For instance, genes are grouped into pathways for particular functions, so that pathways are the higher-level variables and genes within the pathways are the lower-level variables. Higher-level variables are not isolated; instead, they work together to accomplish certain tasks. Therefore, our main interest is to simultaneously explore conditional dependency structures among higher-level variables and among lower-level variables. Given two-level data from heterogeneous classes, we propose a method to jointly estimate the two-level Gaussian graphical models across multiple classes, so that common structures in terms of the two-level conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing higher-level variable factors within classes and then introducing common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantages of our approach using breast cancer patient data. Supplementary materials for this article are available online.

A hierarchical parallel evolutionary algorithm of distributed and multi-threaded two-level structure for multi-satellite task planning

A hierarchical parallel evolutionary algorithm of distributed and multi-threaded two-level structure for multi-satellite task planning

Research on Software Defect Prediction Framework Based on ISFLA in IoT Communication Software

Software defect feature selection has problems of feature space dimensionality reduction and large search space. This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages, the feature values are sorted, and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow. The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks. At the same time, this framework further reduces the dimension of the feature space. After the contrast simulation experiment with other common defect prediction methods, we used the actual test data set to verify the framework for multiple iterations on Internet of Things (IoT) system platform. The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software. This framework can save the testing time of IoT communication software, effectively improve the performance of software defect prediction, and ensure the software quality.

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles.

Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement information output by two sensors enters the tracking processing module, and after processing by a multi-target tracking algorithm, the local tracks are generated and transmitted to the fusion center module. In the fusion center module, a two-level association structure is designed based on regional collision association and weighted track association. The association between two sensors’ local tracks is completed, and a non-reset federated filter is used to estimate the state of the fusion tracks. The experimental results indicate that the proposed algorithm can complete a tracks association between the MMW radar and camera, and the fusion track state estimation method has an excellent performance.

Динамика инвестиционной привлекательности Кемеровской области, 2008–2017 гг.

Investment attractiveness of a region is a complex category, which describes various angles of the state of the socio-economic sphere. Investment attractiveness has two-level structure: organization level and state / region level. At the moment there is no consensus on the essence of this category, and there is also no unified system of indicators characterizing the investment attractiveness of an organization or region. Increasing the investment attractiveness of a region may affect various aspects of life of its population, e.g. by attracting investors to various local projects. The author analyzed scientific literature describing the essence of investment attractiveness and its structure. The paper introduces a system of indicators of investment attractiveness, which includes five groups of indicators that characterize the investment potential of the area. The system is based on scientific publications and system of indicators of investment attractiveness developed by the Expert-RA rating agency. The paper also includes an analysis of the dynamics of the indicators in 2008–2017 and recommendations on how to increase the investment attractiveness of the Kemerovo Region.

Two-level structure swarm formation system with self-organized topology network

In this work, a two-level mobile robot swarm system with self-organized formation network is proposed. Initially, based on the position information of the robots, a relation-invariable persistent formation (RIPF) algorithm can automatically organize the swarm network and construct an optimal persistent formation. At the upper formation planning level, the collision-free reference paths of the swarm can be planned for guiding the robots to reach and maintain a desired distance with their neighbors. Then, at the lower formation tracking control level, a neural-dynamic combined model predictive control (MPC) method is applied to drive the swarm moving on the reference paths. The MPC can reformulate the system into a convex minimization problem, which can further be transformed into a constrained quadratic programming (QP) problem such that an efficient QP solver, called primal-dual neural network (PDNN), is implemented to obtain the optimal control inputs online for the robots. In the end, simulation results show the effectiveness of the proposed formation system.

Adaptive sliding-mode-backstepping trajectory tracking control of underactuated airships

The problem of trajectory tracking control for an underactuated stratospheric airship with model parameter uncertainties and wind disturbances is addressed in the paper. An adaptive backstepping sliding-mode controller is designed from the airship nonlinear dynamics model. The proposed controller has a two-level structure for trajectory guidance, tracking and stability, and the developed controller, based on nonlinear adaptive sliding-mode backstepping method, provides airship attitude and velocity control for the entire flight process. Furthermore, an active set based weighted least square algorithm is applied to find the optimal control surface inputs and the thruster commands under constraints of actuator saturation. The closed-loop system of trajectory tracking control plant is proved to be globally asymptotically stable by using Lyapunov theory. By comparing with traditional backstepping control and PID design, the results obtained demonstrate the capacity of the airship to execute a realistic trajectory tracking mission under two cases of lateral- and roll- underactuations, even in the presence of aerodynamic coefficient uncertainties, and wind disturbances.

Highly Concurrent Latency-tolerant Register Files for GPUs

Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, and large silicon area provisioning. Prior work proposes hierarchical register file to reduce the register file power consumption by caching registers in a smaller register file cache. Unfortunately, this approach does not improve register access latency due to the low hit rate in the register file cache. In this article, we propose the Latency-Tolerant Register File (LTRF) architecture to achieve low latency in a two-level hierarchical structure while keeping power consumption low. We observe that compile-time interval analysis enables us to divide GPU program execution into intervals with an accurate estimate of a warp’s aggregate register working-set within each interval. The key idea of LTRF is to prefetch the estimated register working-set from the main register file to the register file cache under software control, at the beginning of each interval, and overlap the prefetch latency with the execution of other warps. We observe that register bank conflicts while prefetching the registers could greatly reduce the effectiveness of LTRF. Therefore, we devise a compile-time register renumbering technique to reduce the likelihood of register bank conflicts. Our experimental results show that LTRF enables high-capacity yet long-latency main GPU register files, paving the way for various optimizations. As an example optimization, we implement the main register file with emerging high-density high-latency memory technologies, enabling 8× larger capacity and improving overall GPU performance by 34%.

Development of an operational environmental monitoring system for hazardous industrial facilities of Gazprom Dobycha Urengoy

The article presents an assessment of the technogenic impact on the atmospheric air inflicted by oil and gas extracting and processing facilities in Western Siberia. It is revealed that heterogeneity and areal dispersion of emission sources are the key features of the considered industry. Mathematical statistics techniques allowed grouping sources of air pollution and determining the major air pollutants at the considered production facilities. It is proposed to design an automated operational environmental monitoring system with a hierarchical two-level structure for operational and reliable control of the natural environment in the impact area of the studied facilities.

A Low-Profile and High-Gain Frequency Beam Steering Subterahertz Antenna Enabled by Silicon Micromachining

A very low-profile sub-THz high-gain frequency beam steering antenna, enabled by silicon micromachining, is reported for the first time in this paper. The operation bandwidth of the antenna spans from 220 to 300 GHz providing a simulated field of view of 56°. The design is based on a dielectric filled PPW leaky-wave antenna fed by a pillbox. The pillbox, a two-level PPW structure, has an integrated parabolic reflector to generate a planar wave front. The device is enabled by two extreme aspect ratio, 16 x 16 mm large perforated membranes, which are only 30 μm thick, that provide the coupling between the two PPWs and form the LWA. The micromachined low-loss PPW structure results in a measured average radiation efficiency of -1 dB and a maximum gain of 28.5 dBi with an input reflection coefficient below -10 dB. The overall frequency beam steering frontend is extremely compact (24 x 24 x 0.9 mm) and can be directly mounted on a standard WM-864 waveguide flange. The design and fabrication challenges of such high performance antenna in the sub-THz frequency range are described and the measurement results of two fabricated prototypes are reported and discussed.

A Simplified Common-Mode Voltage Suppression and Neutral-Point Potential Control for the NPC Three-Level Inverter

Neutral-point potential unbalancing is an inherent disadvantage of NPC three-level inverter. Traditional control methods, e.g. the space vector PWM (SVPWM) considering balance factor or the model predictive control (MPC), usually needs large amounts of calculation and the common-mode voltage usually keeps large due to the zero vectors being involved in the modulation or the roll optimization. Aiming at above problems, this paper proposes a simplified model predictive algorithm based on the characteristics analysis on the unbalancing and common-mode voltage. Firstly, an approximate two-level structure containing 12 vectors is proposed by the reduced-order processing for the three-level structure, which solves the calculation complexity problem. Meanwhile, multi-constraints including switching frequency, neutral-point potential deviation and the common-mode voltage, are constructed within the roll optimization items of the MPC scheme to make common-mode voltage being restricted less than 1/6 DC voltage while the switching frequency is almost constant. Finally, experiments were carried out to verify that this method can realize a neutral-point voltage balance and a low common-mode voltage along with less execution time.

Health-Related Social Control Influences the Physical Activity of College Students

Objective: To examine the impact of health-related social control tactics on the physical activity behavior of college students. Participants: 110 undergraduates (74% women) recorded their naturalistic behavior across eight consecutive days between March and May of 2010. Methods: A short-term longitudinal daily diary survey examined daily self-reports of received health-related social control (the direct regulation of a target’s health behaviors by a social partner) and minutes engaged in physical activity. Results: Multilevel modeling according to a two-level structure in which observations (Level 1) were nested within individuals (Level 2) revealed that positive social control for exercise (e.g., encouragement) had a positive effect on physical activity participation, both within-persons (intra-individual level) and between-persons (inter-individual level). Negative social control (e.g., nagging) had no effect on physical activity participation. Conclusion: The present results suggest that individuals should employ positive social control tactics when attempting to enhance the physical activity behaviors of college students.

Optimum bi-level hierarchical clustering for wireless mobile tracking systems

A novel technique is proposed to optimize energy efficiency for wireless networks based on hierarchical mobile clustering. The new bi-level clustering technique minimizes mutual interference and energy consumption in large-scale tracking systems used in large public gatherings such as festivals and sports events. This technique tracks random movements of a large number of people in a bounded area by using a combination of smart-phone Bluetooth and Wi-Fi connections. It can be effectively used for monitoring health conditions of crowd members and providing their locations and movement directions. An integer linear programming (ILP) model of the problem is formulated to optimize the formation of clusters in a two-level hierarchical structure. In order to evaluate the proposed technique, it is compared to the optimum solutions obtained from the ILP model for both single-level and two-level clustering. Moreover, a Matlab/Simulink simulation model is developed and used to test the technique’s performance under realistic operating conditions. The results demonstrate a very good performance of the proposed technique.

A novel hierarchical stiff carbon foam with graphene-like nanosheet surface as the desired adsorbent for malachite green removal from wastewater

A novel hierarchical stiff carbon foam (HSCF) was successfully prepared via a carbothermal reduction between the carbon foam with two-level pore structure and the Al2O3 from aluminum sulfate, and used as a bulk adsorbent for removing malachite green (MG) dye. The structures of the HSCF were characterized using SEM, XRD, FTIR, BET, and XPS, and the effects of adsorption condition on the MG removal were studied through batch adsorption experiments. Results show that large-sized and complex-shaped HSCF can be easily fabricated with a high compression strength of 1.58 MPa at a low bulk density (0.10 g cm−3). The HSCF possesses a fluffy graphene-like nanosheet surface with a mesoporous structure and meanwhile exhibits good hydrophilicity loaded with aluminum hydroxide. The experimental maximum adsorption capacity for MG reaches 425.2 mg g−1 with a relatively high partition coefficient of 9.38 mg g−1 μM−1 at the optimal condition. The experimental data are in good agreement with Langmuir isotherm and pseudo-second-order kinetic model, and meanwhile, the adsorption of MG onto the HSCF is a spontaneous and endothermic process. Also, the HSCF still exhibits good adsorption ability and stability after seven regeneration cycles.

Integrated yaw and rollover control based on differential braking for off-road vehicles with mechanical elastic wheel

Aiming at the issue of yaw and rollover stability control for off-road vehicles with non-pneumatic mechanical elastic wheel (MEW), an integrated control system based on fuzzy differential braking is developed. By simplifying the structure of the MEW, a corresponding fitting brush tire model is constructed and its longitudinal and lateral tire force expressions are set up, respectively. Then, a nonlinear vehicle simulation model with MEW is established to validate the proposed control scheme based on Carsim. The designed yaw and rollover control system is a two-level structure with the upper additional moment controller, which utilizes a predictive load transfer ratio (PLTR) as the rollover index. In order to design the upper integrated control algorithm, fuzzy proportional-integral-derivative (PID) is adopted to coordinate the yaw and rollover control, simultaneously. And the lower control allocator realizes the additional moment to the vehicle by differential braking. Finally, a Carsim-simulink co-simulation model is constructed, and simulation results show that the integrated control system could improve the vehicle yaw and roll stability, and prevent rollover happening.