System Design Research Articles

A Novel Free-Air Diesel and Ozone Enrichment (FADOE) Research Platform

Air pollution is an escalating concern in the modern world, posing substantial threats to ecosystems processes. While the importance of comprehending the impact of pollutants on natural environments is evident, conducting rigorous field-based experiments presents formidable challenges. Elevating pollutant concentrations within open air environments in a controlled manner is complex. Nonetheless, such real-world experiments are invaluable for revealing the genuine influence of air pollutants on ecosystems and their functioning. Field-scale measurements have emerged as a pivotal avenue for advancing our understanding of the interactions between air pollutants and the natural world, providing unique insights into ecosystem dynamics, including critical processes like pollination and natural pest regulation. In atmospheric and ecological research, free-air exposure systems have proven effective in elevating carbon dioxide (CO2) and ozone (O3) concentrations, facilitating the exploration of their ecological consequences. Yet, nitrogen oxides (NOx), a class of pollutants with significant ecological and atmospheric relevance, have largely eluded field-based ecological investigations. This paper introduces the recently developed FADOE (Free-Air Diesel and Ozone Enrichment) platform, which allows the elevation of O3 and diesel exhaust (including NOx) within a field-scale context. Comprehensive information on the system's design, construction, and performance data from the 2023 summer season is presented.• Air pollution and ecosystem functioning• Elevated ozone and nitrogen oxides (NOx)• Free-air exposure systems for field scale measurements.

Do laundry when the sun shines: Factors that promote loadshifting in Dutch households with solar panels

The installation of solar panels by residential households is vital for the energy transition. However, the rapid uptake of solar panels by households leads to congestion in the electricity grid. Specifically, when the sun shines, these solar panels simultaneously produce a lot of electricity that is fed into the grid, which is inefficient and can destabilize the grid. Consequently, it is better if self-produced solar energy is directly consumed when the sun is shining. Loadshifting involves shifting energy use (e.g., doing laundry) to periods in which energy is produced. This necessitates behavior change within the household, and it is not yet well understood why people struggle to loadshift. To assess the individualistic and contextual factors influencing laundry loadshifting behavior in the Netherlands, we conducted a survey among 283 Dutch households with solar panels. The survey builds on a framework that integrates aspects of the theory of planned behavior and social practice theory. The framework comprises eight factors (sufficiency attitude, user beliefs, know-how, monitoring skills, habits, hassle, practical knowledge provided, and feedback provision by system design), which are quantitatively measured and analyzed. We used multiple regression analysis to explore the collected responses. Results show the relevance of monitoring skills, strong habits, passive user beliefs and practical knowledge for laundry loadshifting behavior. Findings highlight that instead of asking people to adjust to technologies, technologies should support behavior change and understand its intricacies and connections to its broader context. Additionally, it is important to strengthen households' beliefs regarding their active role in the energy system.

Performance analysis of different control models for smart demand–supply energy management system

Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

Sustaining Digital Assets Through Mobile Estate Planning

Many people own online accounts, with some having financial values like Internet banking, e-wallet and cryptocurrency. In the case of sudden death, their heirs are unaware of the digital assets possessed by the deceased person, which causes the assets to be lost forever, and the heirs might not receive the assets. If an estate plan did not account for digital assets properly, the beneficiaries would not be able to access them. Therefore, this paper addresses this issue by implementing a software development approach in designing a suitable model for sustaining digital assets through smartphones to allow the inheritance of digital assets to the heirs. The software development life cycle was adopted as the methodology to identify the model’s components through four phases: requirements analysis, system design, implementation and test and evaluation. First, this work proposed a model named Mobile Estate Planning for Digital Assets (MEPDA). Second, the study demonstrated the component of the model in a smartphone application, and finally, it was tested and evaluated through a usability study with a group of users. Most users suggested that MEPDA is useful and easy to use. Further, they were also satisfied with its functionalities and features. Mobile application for digital asset estate planning is necessary today due to the exponential increase of digital asset ownership with financial values. It could be one way of helping users to allow an efficient inheritance process for their heirs in the case of death.

Biomechanical investigation of a customized interspinous spacer system in the treatment of degenerative disc diseases: A finite element analysis

BackgroundA novel interspinous fixation system based on anatomical parameters and incorporating transfacetopedicular screws, was developed to treat degenerative disc diseases. The biomechanical characteristics of the novel system were evaluated using finite element analysis in comparison to other classical interspinous spacers. MethodsThe L1-S1 lumbar spine finite element models were surgically implanted with the novel system, Coflex and DIAM devices at the L4/L5 segment to assess the range of motion, the pression distribution of intervertebral disc, the peak stresses on the spinous process and implant during various motions. FindingsRange of motions of the L4/L5 surgical segment were reduced by 29.13%, 61.27%, 77.35%, 33.33%, and the peak stresses of intervertebral disc were decreased by 36.82%, 67.31%, 73.00%, 69.57% for the novel system in flexion, extension, lateral bending, and axial rotation when compared with the Coflex, and they were declined by 34.53%, 57.86%, 75.81%, 25.21%; 36.22%, 67.31%, 75.01%, 71.40% compared with DIAM. The maximum stresses of the spinous process were 29.93 MPa, 24.66 MPa, 14.45 MPa, 24.37 MPa in the novel system, and those of Coflex and DIAM were 165.3 MPa, 109 MPa, 84.79 MPa, 47.66 MPa and 52.59 MPa, 48.78 MPa, 50.27 MPa, 44.16 MPa during the same condition. InterpretationCompared to other interspinous spacer devices, the novel interspinous fixation system demonstrated excellent stability, effectively distributing load on the intervertebral disc, and reducing the risk of spinous process fractures. The personalized design of the novel interspinous fixation system could be a viable option for treating degenerative disc diseases.

Probabilistic design and optimization of thermal protection system with variable thickness based on non-uniform aerodynamic heating

Thermal Protection System (TPS) is a critical component of space vehicles to protect them from damage by extreme aerothermal heating conditions. The uncertainty of the aerothermal heating conditions, including approaching velocities, atmospheric density, pressure, etc., significantly affects the aerothermal heating imposed on the TPS, vastly changing the initial design conditions. In this study, we developed a new uncertainty quantitative (UQ) analysis method to comprehensively consider all the uncertainty of TPS materials, structures, and aerothermal heating conditions. To address the challenges of a massive amount of input and determined optimization parameters, we first induced the response surface method and Monte Carlo algorithm to produce a large number of analysis samples for Probabilistic Design. Then, the key affecting factors, not all the input design parameters, were chosen for optimization with sequential optimization and reliability assessment (SORA) strategy by a sensitivity analysis. With such improvements, the optimization for UQ analysis becomes practical. We used this new UQ method to analyze the effects of uncertainty of the aerothermal heating conditions on the thermal protection layer. A variable thickness design is proposed to adapt the non-uniform aerothermal heating conditions for the TPS. The results show that significant light-weighting can be achieved without the reliability penalty. The optimized structure also reduces internal temperature gradients and facilitates a more uniform temperature field distribution for the TPS. This study provides a new UQ engineering design approach for the system with many designed parameters.

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.

Design of smoke detection system using deep learning and sensor fusion with recursive feature elimination cross-validation

The fire safety system is an important component that controls material and immaterial losses. Fire disasters are generally indicated by the appearance of excess smoke and changes in temperature, pressure, and changes in other parameters in the environment. Conventional smoke sensors are limited in reading parameter changes around their environment, making them less effective in early fire detection. This study aims to design a smoke detection system as an early fire detection system, using sensor fusion based on deep learning using the recursive feature elimination method with cross-validation (RFECV) using a random forest classifier used to select optimal parameters from public datasets as the basis for determining the sensor to be used. Based on the RFECV optimal feature, a deep learning algorithm was performed and obtained an accuracy of 0.99, a precision of 0.99, a recall of 1.00, and an F1 score of 0.99, with a latency time of 34.02 μs, which is 71.76% times faster than the original model.

The Design of a Layered Brain-Computer Interface System With Target Identification Module to Control Home Service Robot

The Design of a Layered Brain-Computer Interface System With Target Identification Module to Control Home Service Robot

High-performance Smart Home System based on Optimization Algorithm

Background: With the recent COVID-19 pandemic, people have become increasingly concerned about their physical health. Therefore, the ability to monitor changes in the surrounding environment in real-time and automatically improve the environment has become a current hot topic to improve the overall health level. Objective: This article describes the design of a high-performance intelligent home system that can simultaneously perform monitoring and automatic adjustment functions. Methods: The ESP8266 was used as the core controller, and the DHT11 and G12-04 sensors were used to collect data, such as temperature, humidity, and ambient light intensity. The sampling frequency was increased and the sampled data were processed to improve data accuracy. The sampled data were wirelessly transmitted to a PC or mobile terminal for real-time display. When the sampled data underwent sudden changes, an alert message was sent via the mobile terminal. Based on the real-time changes in ambient light, an improved lighting brightness adjustment algorithm combining bang-bang and single neuron adaptive PID control was used to adjust the lighting brightness. Results: After testing the system designed in this paper and analyzing the errors compared to standard values, the temperature measurement error ranged from 0% to 0.01107%, and the humidity measurement error ranged from 0% to 0.03797%. The improved algorithm was simulated and tested using MATLAB software and compared with traditional PID algorithms and single-neuron adaptive PID algorithms. The improved algorithm did not overshoot during adjustment, and the system reached a steady state much faster than traditional algorithms. Conclusion: The system showed good performance in real-time, stability, and accuracy, fully demonstrating the effectiveness of the devices and algorithms used in the system. This provides ideas for the design and improvement of future smart homes.

Design and analysis of face recognition system based on VGGFace-16 with various classifiers

This research presents a face recognition system based on different classifiers that deal with various face positions. The proposed system involves the extraction of features through the VGG-Face-16 deep neural network, which only extracts essential features of input images, leading to an improved recognition step and enhanced algorithm efficiency, while the recognition involves the radial basis function in support vector machine (SVM) classifier and evaluate the performance of the system. Also, the system is designed and implemented later by using other classifiers; they are K-neareste2 neighbour (KNN) classifiers, logistic regression (LR), gradient boosting (XGBoost), decision tree classifier (DT) and Naive Bayes classifier (NB). The proposed algorithm was tested with the four face databases: AT&T, PINs Face, linear friction welding (LFW) and real database. The database was divided into two groups: One contains a percentage of images that are used for training and the second contains a percentage of images (remainder) which was used for testing. The results show that the classification by RBF in SVM has the highest recognition rate in the case of using small, medium and large databases; it was 100% in AT&T and Real database, while its efficiency appears to be lower when using large-size databases whereas it is 96% in PINs database and 60.1% in LFW database.

Design of Interactive Online Teaching System for Big Data Computer Information System Based on Mobile Sensor Network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Plateform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

System design of a microstrip antenna by dimension and substrates optimization

The microstrip antennas matured and improved over the last 25 years. Throughout these years, the limitations, and specifications of the said antennas have been overcome and significantly improved. Known to be lowprofile, suitable for mobile, and lightweight, these microstrip antennas are the focus of this research. In this paper, the researchers designed a microstrip antenna with varying lengths and substrates. They tested the changes and the effects in microstrip antennas of different lengths, along with altering substrates. To verify the differences, the researchers compared the performance parameters maximum gain (dBi), minimum gain (dBi), and S11 graph on each tested length and changed substrate. The rough set theory was used to determine the optimal design via MATLAB. From there, the researchers analyzed the results gathered and drew their respective conclusions. Additionally, they saw and compared each data result to know what antenna has the best performance parameters. From the results, a change in the dimension will result in a decrease in the said performance parameters. Furthermore, the change in substrate thickness also diminishes these changes.

System reliability-based design of composite frames with concrete – filled steel plate shear walls

Nowadays, many high-rise buildings are constructed from concrete-filled steel tubular (CFST) columns and concrete-filled steel plate (CFSP) shear walls due to their superior merits. However, these composite buildings are still designed by traditional load and resistance factor design (LRFD) approach which is conservative and impossible to explicitly capture the complex behaviour of CFST structures. Hence, this paper develops an efficient framework for the system design of CFST structures utilizing nonlinear simulation and reliability analysis. Firstly, a C++ source code for a new material model is developed in OpenSees to capture the behaviour of semi-rigid connections. This material is implemented into the numerical model of CFST structures. Secondly, a framework based on subset simulation is developed for the reliability analysis. This code rigorously considers uncertainties of variables such as numerical model, geometric and material properties or applied loads of both American (US) and Australian (AS) standards. Finally, case study and parametric study are conducted by using the developed framework to propose resistance reduction factors for the investigated frames. The results of the study show that the resistance reduction factor can be taken as 0.8 for both AS and US codes. This proposed factor can be a great source of reference for the system design of CFST buildings with composite shear walls.

The development of low-cost spin coater with wireless IoT control for thin film deposition

A low-cost spin coater with a wireless remote system that can deposit thin films of uniform thickness and quality at a significantly lower cost than traditional methods. The system consists of three main parts, a motorized spindle, a spin-coating head, and a control system connected to the network. The mechanical design on the mechanical part, spin coater system design with ESP32, and implementation of wireless control through visual basic. The network-enabled control system allows for real-time monitoring and adjustment of the deposition process, which can improve efficiency and reproducibility. This low-cost spin coating system represents a promising solution for organizations seeking to access thin film deposition technology at a fraction of the cost of traditional systems. By integrating wireless IoT control into low-cost spin coaters, the impact of this technology on coating uniformity will provide valuable insights for future advancements in this field.

Student Result Analysis and Management

Abstract: Student result analysis and management (SRAM) gives a straightforward interface to support student data. It might be utilised by instructive universities or colleges to keep up the records of students effectively. Student information system manages all sorts of student details, academic related reports results, batch details, and other resource related details too. An effective result analysis system plays a pivotal role in facilitating efficient management of student data and performance assessment. Institutions staff and administrators need to manage students’ information and academic records. For staff by offering analysis, streamlined administrative tasks and efficient records management. For students, it provides easy access to their academic performance and results metrics. This abstract delineates the design and implementation of a robust result analysis system, encompassing a PHP-based backend. The system caters to the needs of teachers, students, and administrators, offering functionalities tailored to each user category. Ultimately, the deployment of the result analysis system on a web server caters to the needs of educational institutions, facilitating efficient management of student data and fostering a conducive environment for academic assessment and improvement

Designing IDS to Analyze Malicious Attacks using Machine Learning

Abstract: This study presents a novel approach to enhance network security through the design and implementation of an Intrusion Detection System (IDS) employing machine learning (ML) techniques. The primary aim is to address current challenges in real-time threat detection by seamlessly integrating ML models, including Logistic Regression, Random Forest, and XGBoost. The research tackles drawbacks in existing systems, emphasizing the struggle with real-time threat detection, the complexity of ML integration. The working model classifies incoming requests as normal or intrusions, categorizing the latter into DDoS, R2L, U2R, or Probing attacks. Leveraging insights from four referenced papers, this study builds on efficient activation functions, optimized feature selection, and empirical analyses of ML models for adaptive network intrusion detection. Results showcase promising comparisons between ML algorithms which include Logistic Regression, Random Forest, XG Boost

Optimization of Multi-Gate System in Casting Process Based on Experimental Study

Abstract: This paper proposes a gating system design for casting process using an experimental study. The principle behind multi-gate system optimization is to eliminate defects such as compression gas inclusions, cold shorts and mixing etc. Industrial and research experience shows that the flow of molten metal in the gating system before entering the extraction cavity significantly influences the quality of the casting. In general, slow filling causes defects like cold shirt and mixing while fast filling causes inclusion of sand and causes defects like holes. A gating system consists of runners, sprue gates and filters. The position, number, size, and shape of these elements play an important role. A systematic methodology for gating design optimization considering filling rate maximization has been developed based on limiting constraints. These include pouring time, gating ratio, modulus of ingate, mold erosion, Reynolds number at ingate section and filling rate of molten metal. Molten metal kinematics viscosity similar to that of liquid water can be used to determine the fillings and flowrate of the molten metal in the mold cavity. Aluminum generally has the same kinematics as water. Thus the water flow can be studied by adding the necessary design improvements to the multi-gate system, from which it will be easier to detect defects and will be easier to detect defects and will greatly improve the overall casting quality

Design of a Sustainable PV System to Achieve Net-Zero Energy in Greenhouses

Design of a Sustainable PV System to Achieve Net-Zero Energy in Greenhouses

Ethical keylogger solution for monitoring user activities in cybersecurity networks

This paper presents the development of a software keylogger to monitor user activities within a cyber network, enhancing security and facilitating comprehensive cybersecurity analysis. Guided by the agile methodology, the work adopts a flexible and iterative approach to development, ensuring continuous collaboration, adaptation to evolving requirements, and swift response to feedback. Leveraging the versatility and efficiency of the Python programming language, this work transforms the traditional notion of a keylogger, which is a program that records keystrokes, into an ethical and legally compliant tool. By capturing and logging keystrokes, the system provides valuable insights into user behavior and potential security threats, enabling proactive measures to prevent cyber-attacks and data breaches. Prioritizing transparency and consent, the system design adheres to ethical standards and privacy regulations, ensuring the protection of sensitive information and user privacy. This technical solution offers cybersecurity professionals, system administrators, and organizations a valuable tool for network monitoring while showcasing responsible development practices and contributing to the advancement of ethical cybersecurity solutions.