System Module Research Articles

The second-generation real-time ecological environment prediction system for the Guangdong–Hong Kong–Marco Greater Bay Area: Model setup, validation, improvements, and online visualization

With the rapidly growing population and socioeconomic development of the Guangdong–Hong Kong–Marco Greater Bay Area of China, inputs of diverse contaminants have rapidly increased. This poses threats to the water quality of the Pearl River Estuary (PRE) and adjacent seas. To provide valuable information to assist the governors, stakeholders, and decision-makers in tracking changes in environmental conditions, daily nowcasts and two-day forecasts from the ecological prediction system, namely the Coupled Great Bay Ecological Environmental Prediction System (CGEEPS), has been setup. These forecast systems have been built on the Coupled Ocean–Atmosphere–Wave–Sediment Transport modelling system. This comprises an atmospheric Weather Research Forecasting module and an oceanic Regional Ocean Modelling System module. Daily real-time nowcasts and 2-day forecasts of temperature, salinity, NO2 + NO3, chlorophyll, and pH are continuously available. Visualizations of the forecasts are available on a local website (http://www.gbaycarbontest.xyz:8008/). This paper describes the setup of the environmental forecasting system, evaluates model hindcast simulations from 2014 to 2018, and investigates downscaling and two-way coupling with the regional atmospheric model. The results shown that though CGEEPS lacks accuracy in predicting the absolute value for biological and biogeochemical environmental variables. It is quite informative to predict the spatio-temporal variability of ecological environmental changes associated with extreme weather events. Our study will benefit of developing real-time marine biogeochemical and ecosystem forecast tool for oceanic regions heavily impact by extreme weathers.

Uncertainty Propagation in UAM Time-Dependent Neutronics PWR Studies

A subset of pressurized water reactor (PWR) studies from Exercise II-2 of the Uncertainty Analysis in Modeling for Light Water Reactors (UAM-LWR) benchmark study has been performed to quantify the importance of both nuclear data uncertainties and manufacturing uncertainties in an assembly depletion calculation and a mini-core rod movement transient. The depletion study of the 15 × 15 PWR assembly using the SAMPLER and TRITON modules of the SCALE code system revealed a maximum uncertainty in keff of 0.49% for fresh fuel, decreasing to 0.38% at the midpoint of the fuel cycle and rising back to 0.48% at the end of the fuel cycle. Uncertainties and correlations of various homogenized cross sections and other group constant data, such as keff, have been determined, and the effect of randomly applied manufacturing uncertainties was found to be largely negligible relative to nuclear data uncertainties for bulk lattice parameters. However, for local parameters, such as the pin power factors, assembly discontinuity factors, and diffusion coefficients, the effects from manufacturing uncertainties were appreciable and sometimes dominant. Nuclear data uncertainties were found to be the dominant contributors to uncertainty in the isotopic composition of the overall assembly, with the exception of very early in the fuel cycle, where manufacturing uncertainties such as perturbations to the fuel density and pin radius made nonnegligible contributions to total uncertainty. The contribution of manufacturing uncertainties to isotopic uncertainties was nonnegligible at a pin-by-pin level, but still smaller than the contributions from nuclear data uncertainty. Studies of the PWR mini-core rod movement transient using homogenized data from the SCALE models in the PARCS diffusion code showed little difference between the tested modeling approaches and demonstrated that nuclear data uncertainties dominated the manufacturing uncertainties in the global figures of merit considered, such as the equilibrium core boron concentration, the maximum core power factor, and the maximum reactivity insertion. For local effects, such as maximum pin power during the transient, the randomly applied manufacturing uncertainties were dominant. It was found in general that for global system properties, nuclear data uncertainties made significantly larger contributions to total uncertainty, whereas for local parameters the impact of manufacturing uncertainties was at least nonnegligible, and for some parameters, dominant.

Presynaptic spike-driven plasticity based on eligibility trace for on-chip learning system.

Recurrent spiking neural network (RSNN) performs excellently in spatio-temporal learning with backpropagation through time (BPTT) algorithm. But the requirement of computation and memory in BPTT makes it hard to realize an on-chip learning system based on RSNN. In this paper, we aim to realize a high-efficient RSNN learning system on field programmable gate array (FPGA). A presynaptic spike-driven plasticity architecture based on eligibility trace is implemented to reduce the resource consumption. The RSNN with leaky integrate-and-fire (LIF) and adaptive LIF (ALIF) models is implemented on FPGA based on presynaptic spike-driven architecture. In this architecture, the eligibility trace gated by a learning signal is used to optimize synaptic weights without unfolding the network through time. When a presynaptic spike occurs, the eligibility trace is calculated based on its latest timestamp and drives synapses to update their weights. Only the latest timestamps of presynaptic spikes are required to be stored in buffers to calculate eligibility traces. We show the implementation of this architecture on FPGA and test it with two experiments. With the presynaptic spike-driven architecture, the resource consumptions, including look-up tables (LUTs) and registers, and dynamic power consumption of synaptic modules in the on-chip learning system are greatly reduced. The experiment results and compilation results show that the buffer size of the on-chip learning system is reduced and the RSNNs implemented on FPGA exhibit high efficiency in resources and energy while accurately solving tasks. This study provides a solution to the problem of data congestion in the buffer of large-scale learning systems.

The Development of Mobile AR-Based Module for Teaching and Learning Pneumatic System: A Needs Analysis

Practical skills and theoretical knowledge are vital competencies that technical and vocational trainees need to master before joining the industrial workforce to perform tasks efficiently. Therefore, comprehensive instructional materials and training equipment are essential to ensure that the skills and knowledge are delivered effectively to trainees. This study aimed to identify the need to develop a mobile augmented reality module to teach and learn pneumatic systems in Malaysian vocational colleges. Descriptive research was conducted to determine the module’s need, involving 13 subject-matter experts from various vocational colleges. A set of questionnaires consisting of three main themes: the need for the module, the module’s user interface design, and the module’s usability criteria with 49 items was used. The data collected were analysed using the Fuzzy Delphi Method. All three constructs showed high levels of expert consensus of more than 75% and a threshold value of ≤ 0.2. Therefore, experts agreed that there was a need to develop a mobile AR-based module for teaching and to learn pneumatic systems with the stated user interface designs and usability criteria.

Vector-variable based modeling method for large-scale wind farm considering collector system dynamics

Different from conventional power plants, a wind farm usually consists of hundreds of wind generators and collector systems with arbitrary topology. The numerous devices increase modeling efforts and computational burden. This paper proposes a vector-variable based method for dynamic modeling of the large-scale wind farm considering collector system dynamics. Using the vector variables, the modeling method converts the complex wind farm containing many components to a model consisting of two typical submodules, the wind generator module and the collector system module. These submodules can establish any number of wind generators and any topology of the collection systems considering dynamics freely, with good scalability. The correctness and high simulation efficiency of the established model are validated by the electromagnetic model. Furthermore, a detailed wind farm containing twenty-five wind generators is built via the proposed method. Compared with the aggregation model, the results show that the established model can reflect the wind farm stability accurately. Also, by comparing with the model neglecting the collector system dynamics, the results reveal the relative error caused by neglecting the collector system dynamics can lead to misjudgment of instability mode when analyzing the sub-synchronous modes.

Vision-Based Ingenious Lane Departure Warning System for Autonomous Vehicles

Lane detection is necessary for developing intelligent Autonomous Vehicles (AVs). Using vision-based lane detection is more cost-effective, requiring less operational power. Images captured by the moving vehicle include varying brightness, blur, and occlusion caused due to diverse locations. We propose a Vision-based Ingenious Lane Departure Warning System (VILDS) for AV to address these challenges. The Generative Adversarial Networks (GAN) of the VILDS choose the most precise features to create images that are identical to the original but have better clarity. The system also uses Long Short-Term Memory (LSTM) to learn the average behavior of the samples to forecast lanes based on a live feed of processed images, which predicts incomplete lanes and increases the reliability of the AV’s trajectory. Further, we devise a strategy to improve the Lane Departure Warning System (LDWS) by determining the angle and direction of deviation to predict the AV’s Lane crossover. An extensive evaluation of the proposed VILDS system demonstrated the effective working of the lane detection and departure warning system modules with an accuracy of 98.2% and 96.5%, respectively.

Scaling Automated Programming Assessment Systems

The first automated assessment of student programs was reported more than 60 years ago, but this topic remains relevant and highly topical among computer science researchers and teachers. In the last decade, several factors have contributed to the popularity of this approach, such as the development of massive online courses, where large numbers of students can hardly be assessed manually, the COVID-19 pandemic with a strong online presence and physical relocation of students, and the ever-increasing shortage of personnel in the field CS. Modern Automated Programming Assessment Systems (APASs) are nowadays implemented as web applications. For such web applications, especially those that support immediate (on-demand) program assessments and feedback, it can be quite a challenge to implement the various system modules in a secure and scalable manner. Over the past six years, we have developed and actively deployed “Edgar”—a state-of-the-art APAS that enables immediate program evaluation and feedback in any programming language (SQL, C, Java, etc.). In this article, we look at the APAS web application architecture with a focus on scalability issues. We review fundamental features such as dynamic analysis and untrusted code execution, as well as more complex cases such as static analysis and plagiarism detection, and we summarize the lessons learned over the previous six years of research. We identify scalability challenges, show how they have been addressed in APAS Edgar, and then propose general architectural solutions, building blocks and patterns to address those challenges.

E-Module Learning Media Ignition System Material Based on Flipbook

Background:The flipbook-based ignition system e-module is an electronic ignition system module that includes ignition system material whose material is equipped with pictures, video explanations of material and interactive quizzes that can be applied on line using a smartphone or computer without having to download it first.
 Methodology:This type of research is Research & Development (R&D) with the adaptation of the Dick and Carey model using data collection instruments in the form of questionnaires and expert reviews, while the data analysis technique uses qualitative descriptive analysis techniques and descriptive statistical analysis.
 Findings:The results showed the feasibility of the product with a percentage figure above 85% (very feasible).
 Conclusion:The developer concludes that the ignition system e-module development product based on flipbooks can be used as a learning medium in the subject of Light Vehicle Electrical Maintenance in class XI TKRO ignition system material, as evidenced by the results of questionnaires and comments from experts that are very good/appropriate with the percentage figures above. 85%
 Originality:This flipbook-based ignition system e-module development product has been validated by material experts, media experts, design experts, and colleagues who all meet the minimum educational criteria for a master's degree in their respective fields, and have gone through individual trials, small group trials, and revised field trials.

Research on Intelligent System of Computer Aided Ideological and Political Course Teaching

This paper focuses on the design scheme of the auxiliary teaching system for ideological and political theory courses using J2EE architecture design. In this paper, the overall planning of the system's architecture, work flow, system function, data structure, etc. is carried out, and it is designed and implemented in detail. The system adopts B/S three-tier structure and uses JSP technology to design dynamic pages. Consider using JavaBean to encapsulate the key code of the program from the aspects of system security and code reusability. Use CASE tools and object-oriented modeling language in system design. Through UML modeling language, use VISIO, ROSE and other modeling tools to analyze and design the system to generate conceptual models and entities of the system. Through requirement analysis and detailed design, the user login module, network course module, online examination module, learning resource module and exchange forum module are finally realized. Finally, the thesis assists the realization of the functions of each module of the teaching system, and carries out corresponding tests and verifications, and the system is in the testing stage and plays a great role in practice.

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.

Crow Sun Flower Optimization-Based Handover Modules in 5G Networks

Handover modifies the user equipment using mobility in which base station provides the best one. The repeated handovers may corrupt mobility reliability due to high signaling load and therefore, network capability enhancement is affected. Here, a network management system in a network is difficult one owing to the rising number of complexity issues and base stations. In this paper, Crow Sun Flower Optimization (CSFO)-based handover method is developed for enabling efficient handover in Fifth Generation (5G) network. This handover method mainly consists of four parts, such as User Preference (UP) section, Network Quality of Service (NQ) module, power section, and Decision System (DS) module. The Quality of service (QoS) is controlled by UP section and NQ module, whereas the power module is concentrated on power. Thus, the handover is decided based on three segments and DS module is used to enable the network. The DS module is effectively decided whether to offer handover in 5G network or not. Moreover, the decision is optimally selected based on an optimization technique, named as CSFO algorithm. The developed CSFO technique is newly designed by integrating Crow Search Algorithm (CSA) and Sun Flower Optimization (SFO) technique. Additionally, three performance indicators, including received power, throughput, and user-served ratio, are used to assess how well the created CSFO-based handover model performs. High received power, throughput, and user served ratio of [Formula: see text][Formula: see text]dBm, [Formula: see text][Formula: see text]kbps and 0.071, respectively, are achieved by the developed handover strategy.

Analysis of Strategic Factors for Successful Integrated System Implementation: Experiences from the Co-operative University of Kenya

An integrated resource planning (ERP) system is essential for automation of manual routine tedious organizational processes. It is meant to improve efficiency and effectiveness at work. However, implementation of such systems, if not properly organized and appropriate strategies put in place to ensure its success, may take unnecessarily longer time than anticipated, or the whole system abandoned all together. This study sought to establish the most effective strategies that ensure successful and timely integrated system implementation. Systems theory and Process theory approaches were used to anchor the study. Case study was adopted as the research design. The population of the study comprised twelve (12) departments within Co-operative University of Kenya where different system modules were rolled out for implementation. Primary data was collected using semi-structured questionnaires. Since data collected was largely categorical in nature, Chi-square was used to analyze relationship between variables. It was established that personal interest and optimism is the most influential strategy in determining success and pace in integrated system implementation. The key recommendation that the study offers as insight to policy makers, body of theory and practitioners is the need to deliberately concentrate on strategies that induce personal interest and optimism among employees so as to ensure success during implementation of a new system or change.

Event Building Algorithm in a Distributed Stream Processing Data Acquisition Platform: D-Matrix

Event building is an important task for data acquisition (DAQ) systems in high-energy physics experiments. D-Matrix is designed as a generic distributed stream processing DAQ platform, containing hardware description language (HDL)-based modules and software modules. In the D-Matrix system, the data transfer is abstracted into the form of a stream, and the event building is implemented through a cascade of various reusable stream processing modules. These stream processing modules will determine their work modes automatically based on a set of attributes, which describe the abstracted data stream characteristics, such as spatiotemporal organization and granularity. This article introduces the event building architecture, the physics data structure abstraction, and the algorithmic implementation of specific event building modules in the D-Matrix system. An application of the D-Matrix event building architecture in the CSR external-target experiment is presented in the end.

Research on parking guidance system based on computer vision

The rapid pace of urbanization has made the urban parking problem more and more important, and various regions have introduced corresponding policies and measures to solve the parking problem, such as building large parking plants, introducing intelligent systems, etc. However, the number of parking spaces in large parking plants is large, the spatial distribution is complex, and it is difficult to find parking spaces. It is very important to improve parking efficiency through intelligent parking guidance and management. The parking space recognition system based on image recognition technology of computer vision can obtain real-time parking space utilization. The vehicle guidance system based on the generalized shortest path can guide incoming vehicles, reduce the time for vehicles to wait and find parking spaces. As the core module of the parking intelligent management system, the intelligent parking space recognition system and vehicle guidance system can significantly improve the parking efficiency.

Impact of the polarization control system on continuous-variable quantum key distribution system parameters

Subject of study. This study considers a polarization control method of optical radiation for a continuous-variable quantum key distribution system for correct polarization demultiplexing of the local oscillator and signal state necessary to ensure the correct performance of the presented system. Aim of study. This study aims to develop a system of active polarization control and experimentally evaluate the effect of this system on the continuous-variable quantum key distribution system parameters. Method. The polarization control system considered in this study is based on constant analysis of the local oscillator radiation and maximization of its intensity due to the stochastic gradient descent algorithm. Local oscillator fluctuations occur because of the principle of active polarization control operation. An experimental setup simulating the sender and receiver module of the continuous-variable quantum key distribution system is used to evaluate the introduced noise. This setup enables evaluation of the noise introduced by the controller operation. Main results. Excess noise caused by the polarization controller is comparable with the polarization noise generated in the quantum key distribution system. Practical significance. Correct operation of a continuous-variables quantum key distribution system can be implemented exclusively with the use of a polarization control device. Knowledge of the polarization control effect on the system gains an understanding of the possible future methods for designing and developing continuous-variables quantum key distribution systems.

Development of DC Parameter Calibration Module for Digital Integrated Circuit Test System

Aiming at the on-site measurement requirements of the integrated circuit test system, this paper designs the DC parameter calibration module of the digital integrated circuit test system based on STM32 single-chip microcomputer as the core control, studies the methods of precision improvement based on multiple groups of devices, noise control, and error compensation, reduce the volume of the device, simplifies the measurement loop in the calibration process, and improves the DC parameter calibration accuracy of the digital channel of the integrated circuit test system, improve the portability and ease of use of the calibration device, and lay the foundation for the calibration device of the new generation integrated circuit test system.

Early coronavirus disease detection using internet of things smart system

<p>The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.</p>

ПРОГРАММНАЯ РЕАЛИЗАЦИЯ ЗАЩИТЫ ОТ НЕСАНКЦИОНИРОВАННОГО ДОСТУПА

Information security tools are an integral part of system users. The concept of information security implies the development and expansion of the scope of innovative technologies in information processing. To keep the information security system up to date, it is necessary to periodically update and supplement the structure of information protection, information security threat model and hardware and software complex. This paper analyzes the existing methods of information protection and proposes the implementation software system modules for personality recognition by photo and video, voice recognition, deep neural network and the creation of configuration for its weights file. Based on the generated data set, a method for synthesizing the parameters of a mathematical model of a convolutional neural network, presented as an array of real numbers, which are unique identifiers of a personal computer user, has been developed and proposed. This study uses the features of simulation modeling of user authorization systems, as well as the error function when compiling a neural network model. The training model of multi-factor biometric authentication is trained using categorical cross-entropy. The training sample is generated by adding distorted images from the database by changing the receptive fields of the convolutional neural network. The objective of this study is the application of new methods and means of protecting information of workstations from information threats. The result of the study is the developed information security system designed to ensure the information security of users of personal computers and workstations of enterprises.

Model-Based Coordinated Trajectory Tracking Control of Skid-Steer Mobile Robot with Timing-Belt Servo System

Four-wheel, independently driven skid-steer mobile robots have been widely used in some fields, such as indoor product shipping and outdoor inspection and exploration. Traditional skid-steer mobile robot controllers often use a kinematics controller to obtain the desired speed of each wheel, complete speed closed-loop control of each wheel and achieve the robot’s trajectory tracking control. However, the controller based on kinematics may lead to robot chattering and wheel spin from being directly driven by the motor on uneven grounds. To solve these problems, we developed a four-wheel, independently driven skid-steer mobile robot with a damping module for the timing-belt servo system and proposed a model-based coordinated trajectory tracking control method with the timing-belt servo system. First, the kinematics and dynamics of the mobile robot are established, including the chassis kinematics and dynamics, as well as the dynamics of the timing-belt servo system. Secondly, the hierarchical control law is designed, which has adaptive robust control of the upper-level robot chassis, middle-level control allocation approach, and adaptive robust control of the bottom-level timing-belt servo system. Finally, the proposed method is verified by the robot’s trajectory tracking control comparison simulation experiments and has a better control performance.

The study of the radiating module of the energy management system of buildings

Purpose. Improving the toolkit of interaction between sensors and the building control system using a triangular microstrip antenna. Methodology. Mathematical modeling by the method of finite differences in the time domain. Findings. The proposed technical solution consists in choosing the optimal design of the radiating module in the form of a triangular microstrip antenna, by means of mathematical modeling, the values of the geometric dimensions of the triangle, the thickness of the dielectric layer and the value of its dielectric constant, the overall dimensions of the radiating module, which ensure optimal coordination of the antenna with the power feeder in the form of a microstrip line. The developed computer model of the triangular microstrip antenna using the finite-difference method in the time domain allows conducting research on the parameters of this antenna that provide stable wireless communication between the system of sensors and the home automation control unit. The condition for increasing the degree of matching the impedance of the antenna and the supply line is the choice of the method of powering the antenna. It was established that the most optimal way of feeding a triangular antenna is the method of connecting the microstrip line to the middle of the side of the triangle, compared to the way of connecting the power line to the top of the triangle. The width of the microstrip power line was optimized according to the criterion of minimizing the module of the reflection coefficient at the antenna input. A study of the degree of matching the impedance of the antenna at operating frequencies of 2,4 and 7 GHz was carried out. At a frequency of 7 GHz, the magnitude of the reflection coefficient module is -23,6776 dB. The three-dimensional and two-dimensional radiation patterns of this antenna are calculated. The radiation pattern has an almost spherical shape, that is, it allows placing the radiating module in any orientation relative to the earth's surface. This allows the triangular microstrip antenna to be used as a radiating module of the building energy management system and the smart home system. Originality. The study of the ways of feeding the triangular microstrip antenna allows to find the optimal coupling of the antenna with the power line, which ensures the formation of radiation characteristics that ensure effective interaction between the sensors and the building's energy management control system. Practical value. The radiation characteristics of a triangular microstrip antenna obtained as a result of the study make it possible to use it to organize a wireless communication channel in a 4% operating frequency band.