Low Memory Occupation Research Articles

System Design for Online Foreign Language Education Based on Blockchain Technology.

This study aims to solve the problem that the traditional online foreign language teaching system focuses on function development, ignoring system security, and has certain risks. An online foreign language education system is designed and developed based on the blockchain technology. First, the blockchain technology and key technologies of system design are described in detail. Second, the overall technical architecture of the system, functional modules, and business logic of each module are designed. Finally, the basic performance of the system is tested. The results show that the system can realize the user's unrestricted office work and zero maintenance of the system. The separation of presentation logic and business logic facilitates the development and maintenance of the system. The system mainly includes six functional modules: user management, course management, course order, course study, course certificate, and credit authentication. These modules are guaranteed for daily teaching use. The event processing success rate of the six functional modules of the system is greater than 99%, and the processing success rate is relatively high. The central processing unit (CPU) usage and memory usage are both below 30%. The host throughput of the six major modules is greater than 100 times/s when processing services. The average response time on the terminal side is maintained below 0.5 s. The average response time of business-side processing is maintained below 0.4 s, which is in line with the standard. The event processing success rate of the constructed system is 10.75% higher than that of other systems, and the average response time, CPU usage, and memory usage are 53.38%, 51.49%, and 50% lower than other systems, respectively. Therefore, the proposed system has better performance. To sum up, the designed system has excellent throughput, event processing capability, response speed, and low CPU and memory occupancy when processing business and is suitable for promotion and use in foreign language online education in colleges and universities. The use of the proposed system can improve its overall teaching efficiency and quality. The purpose is to provide important technical support for the improvement of the security of the online foreign language teaching system.

Research on open source platform of digital power grid based on CEPH open source distributed storage technology

In order to ensure the operational quality of open source platform and improve the security of power grid information management, a research method of digital power grid open source platform based on CEPH open source distributed storage technology is proposed. The open source platform structure of digital power grid is optimized by using the operation of object to block mapping and human-computer interaction function of CEPH platform. CEPH open source distributed storage is used to optimize the information management function of digital grid open source platform and improve the effect of grid data and information sharing. An adaptive quantization strategy is designed to dynamically determine the load factor weightthe weighty of load factor of the digital grid open source platform and improve the resource storage performance of the platform. The test results show that the digital grid open source platform based on CEPH open source distributed storage technology has low memory occupancy, data processing of high efficiency and good system load balancing ability, which meets the needs of information security and data processing of the digital grid open source platform.

Construction Method of Enterprise-level Database Operational Measurement Platform Based on Bidirectional Coupling Algorithm

When identifying the enterprise database data information, the enterprise database operational dimension measurement platform based on the bilinear method will produce ringing or overshoot effect, the database data monitoring effect is poor as well as the accuracy of operation measurement. To solve this problem, this paper proposes a method to build an enterprise level database operation and maintenance measurement platform based on bidirectional coupling algorithm. Build enterprise database operation measurement platform. The enterprise database monitoring platform is connected with the monitoring database by remote database chain. The Oracle database job scheduling method is used to obtain the monitoring index information in the monitored database, and the database memory performance is comprehensively evaluated by MPI (Memory Perform Index). The platform uses semantic capture layer and related analysis layer to distinguish user behavior, analyze user experience satisfaction, and realize the operation measurement of enterprise level database. The experimental results show that the operational measurement platform built by this method has high throughput, low memory occupancy rate, high measurement accuracy and good user experience.

A Massive Laser Point Cloud Data Organization Strategy Based on the Mixed Model

In the process of data interaction, neighborhood query, filtering, visualization, and dynamic update of LiDAR point cloud data, how to efficiently organize and process massive point cloud data, and quickly index and locate any point in the point cloud and its neighborhood Search is a key issue to be solved urgently. In this paper, combining the advantages of a virtual grid with no interpolation loss on original data, sT spatial relationship, and low memory occupation of the octree, we design an index method based on the combination of virtual grid and adaptive octree based on dynamic scheduling of internal and external memory. Realize the organization and scheduling of massive LiDAR laser scanning point cloud data.

Embedded Model Predictive Control With Certified Real-Time Optimization for Synchronous Motors

Model predictive control (MPC) is a very attractive candidate to replace standard field-oriented control algorithms for electrical motors. We demonstrate that it is possible to implement an MPC algorithm for continuous control set (CCS-MPC), with both inputs and states constraints, in which the associated quadratic programming (QP) problem is solved online, even on the computationally limited platforms used in control of electrical motors. We detail the implementation of an active-set algorithm to solve efficiently the associated QP problem. Moreover, by exploiting recent results on active-set solver certification, we are able to assess the computational complexity of the online optimization algorithm, providing the exact worst-case solution time. The controller is experimentally tested on an embedded control unit for the torque regulation of a permanent magnet synchronous motor and benchmarked against explicit MPC. Computational feasibility, low-memory occupancy, and worst-case certification are achieved, fulfilling all the requirements of embedded control.

State‐of‐charge estimation of power lithium‐ion batteries based on an embedded micro control unit using a square root cubature Kalman filter at various ambient temperatures

The development of a novel method to estimate the state of charge (SOC) with low read-only memory (ROM) occupancy, high stability, and high anti-interference capability is very important for the battery management system (BMS) in actual electric vehicles. This paper proposes the square root cubature Kalman filter (SRCKF) with a temperature correction rule, based on the BMS of a common on-board embedded micro control unit (MCU), to achieve smooth estimation of SOC. The temperature correction rule is able to reduce the testing effort and ROM space used for data table storage (189.3 kilobytes is much smaller than the storage of the MPC5604B, with 1000 kilobytes), while the SRCKF is adopted to achieve highly robust real-time SOC estimation with high resistance to interference and moderate computing cost (68.3% of the load rate of the MPC5604B). The results of multiple experiments show that the proposed method with less computational complexity converges rapidly (in approximately 2.5 s) and estimates the SOC of the battery accurately under dynamic temperature condition. Moreover, the SRCKF algorithm is not sensitive to the high measuring interference and highly nonlinear working conditions (even with 1% current and voltage measurement disturbances, the root mean square error of the proposed method can be as high as 0.679%).

SPEED-3D: a geographic routing protocol for 6LoWPAN networks

The integration of the wireless sensor network WSN technology in the internet world is a necessary step towards the full accomplishment of the internet of things IoT vision. In order to reach a seamless integration of tiny mote devices in internet, the IPv6 addressing method and communication protocols developed for the IoT must be modified or adapted to match the WSN requirements. This adaptation process is started in the 2007 with the standardisation of the IPv6 protocol over low power wireless personal area networks 6LoWPAN. In such a scenario, we propose SPEED-3D, an extension of the SPEED geographic routing algorithm, adapted to 6LoWPAN networks that caters for 3D routing. SPEED-3D avoids the creation of routing tables, and has a very low memory occupation; it also supports load balancing and flow shaping; finally, thanks to the presented extension, it is able to work seamlessly in a 3D setting e.g., a tall building. In the paper, we first present SPEED-3D in 6LoWPAN networks by detailing the encapsulation of all messages in the IEEE802.15.4 medium access control MAC protocol and the new functionality provided thanks to the IPv6 standard, then we present its implementation on a real-time kernel and its performance in a realistic indoor scenario.

Design Considerations of Self-Adaptive Wireless M2M Network Communication Architecture

For the existing heterogeneous wireless M2M network architectures, different data link layers lack a common structure. In this paper, the concept of attribute assembly layer has been proposed, and an adaptive architecture for wireless M2M network has been built, based on the hierarchical heterogeneous modeling of Ptolemy II. Finally, saturated and unsaturated conditions network experiments have been designed in the part of system test. Experimental results show that this architecture has a low memory occupation and less time cost. It unifies the data link layers for heterogeneous networks and is compatible well with the existing platforms, communication protocols, and network mechanisms. The self-adaptive network has more connected sets. The proposed architecture has good adaptive capacity and it can apply to potential communication protocols.

An Adaptive Progressive Mesh Reconstruction Algorithm for Spatial Discrete Points

Triangulation is an important procedure of model generation. Stereo vision can restore the depth of points but only obtain the coordinates of discrete points, which need to be formed into mesh model using surface reconstruction method. This paper proposed an adaptive progressive mesh reconstruction algorithm for spatial discrete points. Through switch of 3D and 2D Delaunay triangulation and adaptive search of the k nearest neighbors, the algorithm can restore the topology of the points and reconstruct the model rapidly. Experimental results show that the algorithm has the ability of adaptive modeling to discrete points of different scale and shape with high efficiency and low memory occupancy.

Tool Cutting Edge Line Detection Based on Improved Hough Transform

In view of the inherent limitations of classical Hough transform in the detection of line, an improved algorithm of randomized Hough transform is offered in this paper. The feature point sets are segmented based on the connected component labels in this algorithm. The elements of various point sets are stored in sequence so as to reducing the invalid samples. By using a valid line detection area and a dynamic storage management, the temporary storage units are gradually reducing with the iterative process. The speed of the line detection and the accuracy of identification can be improved, the storage space can be reduced, the computational complexity can be cut down and some unnecessary calculations can be avoided by using this algorithm. However, it still has the characteristics of the classical Hough transform that it is not sensitive to noise. The line edge where the cutting tool is on can be pinpointed. To achieve the vision-based mode of tool wear state, it can accurately detect rake and flank face of the cutting tool, and can effectively remove the influence to the tool wear detection from the BUILT-UP EDGE in cutting process. By the validation of testing examples, the algorithm has high speed, low memory occupation and high accuracy.

Octree-based, GPU implementation of a continuous cellular automaton for the simulation of complex, evolving surfaces

Presently, dynamic surface-based models are required to contain increasingly larger numbers of points and to propagate them over longer time periods. For large numbers of surface points, the octree data structure can be used as a balance between low memory occupation and relatively rapid access to the stored data. For evolution rules that depend on neighborhood states, extended simulation periods can be obtained by using simplified atomistic propagation models, such as the Cellular Automata (CA). This method, however, has an intrinsic parallel updating nature and the corresponding simulations are highly inefficient when performed on classical Central Processing Units (CPUs), which are designed for the sequential execution of tasks. In this paper, a series of guidelines is presented for the efficient adaptation of octree-based, CA simulations of complex, evolving surfaces into massively parallel computing hardware. A Graphics Processing Unit (GPU) is used as a cost-efficient example of the parallel architectures. For the actual simulations, we consider the surface propagation during anisotropic wet chemical etching of silicon as a computationally challenging process with a wide-spread use in microengineering applications. A continuous CA model that is intrinsically parallel in nature is used for the time evolution. Our study strongly indicates that parallel computations of dynamically evolving surfaces simulated using CA methods are significantly benefited by the incorporation of octrees as support data structures, substantially decreasing the overall computational time and memory usage.

A dynamic programming method with lists for the knapsack sharing problem

In this paper, we propose a method to solve exactly the knapsack sharing problem (KSP) by using dynamic programming. The original problem (KSP) is decomposed into a set of knapsack problems. Our method is tested on correlated and uncorrelated instances from the literature. Computational results show that our method is able to find an optimal solution of large instances within reasonable computing time and low memory occupancy.

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.

A decomposition-heuristic-rule-enabled decision-making system for dynamic quay crane allocation and scheduling

The Quay Crane Scheduling Problem (QCSP) plays an important role in the container terminal operation. In order to develop an effective QCSP model, in this study, the QCSP is decomposed into two sub-problems, i.e., the QC-allocation and QC-scheduling levels. By defining precedence constraints and heuristic rules, dynamic decisions were then made between ships and within a ship. An example of a specific container terminal was applied to validate the dynamic QC allocation and scheduling strategy. It was also revealed from the experimental results that the strategy possessed such properties as automated scheming, low memory occupation and user-friendly interfacing, viz., the operation efficiency, stability and practicality outperformed the existing mode. Therefore, this approach has been proven effective and pragmatic in automated operation and decision-making.

A fuzzy controller for maximum energy extraction from variable speed wind power generation systems

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of wind systems. This paper presents a data-driven design methodology able to generate a Takagi–Sugeno–Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input–output space, combined with genetic algorithms (GA), and recursive least-squares (LS) optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance and learning capability.

Algebraic solution of time-domain nonuniform transmission-line equations by 2-D wavelet transform

In this paper, the numerical solution of nonuniform transmission lines is treated. The method proposed is based on the double wavelet expansion of the multiconductor transmission lines (MTL) equations; the expansion is performed both in the time domain and in the space domain, and the coupled partial differential equations become an algebraic system in the Lyapunov form which is solved by the use of standard techniques. The method has been tested in many cases and has demonstrated to require low CPU time consumption and low memory occupation.

Misfiring Detection in 8 Cylinders Turbo Engine

The paper describes a Heuristic Misfire detection strategy, called MEDOC, based on flywheel speed analysis. Tests carried out by means of numerical simulation show both control algorithm robustness and “easy tuning”. MEDOC low CPU load request, low memory occupation and good results in the actual case study prove that its application in automotive industry can be cost effective and market competitive.