Design Flaws Research Articles

Visualization analysis of urban planning assistant decision network 3D system based on intelligent computing

In recent years, with the continuous development of urban construction and the continuous expansion of urban population, the contradiction between spatial planning and actual urban needs in urban construction has become increasingly acute. However, planning decisions based on traditional urban planning renderings and two-dimensional (2D) planar plans are no longer in line with the actual development needs and specific development status of the city. Urban planning 3D auxiliary systems can improve the accuracy and precision of urban planning decisions. By providing planners with detailed 3D spatial models of the urban environment, potential design defects or conflicts can be identified before urban construction starts. This can reduce costs and risks related to urban development and help create a more livable and sustainable urban environment. This article analyzed and studied urban planning problems based on a three-dimensional (3D) auxiliary system of intelligent computing. In order to examine the effectiveness of intelligent computing 3D auxiliary systems in urban planning and construction, three indicators of development sustainability, transportation network convenience, and urban livability were established for effectiveness evaluation. The final experimental results showed that the average score of the 3D urban planning assistance system based on intelligent computing in urban development sustainability was 9.29 points, transportation convenience score was 92.08 points, and urban livability score was 9.249 points.

Improving and comparing performance of machine learning classifiers optimized by swarm intelligent algorithms for code smell detection

In complex systems, the maintenance phase engenders the emergence of code smells due to incessant shifts in requirements and designs, stringent timelines, and the developer's relative inexperience. While not conventionally classified as errors, code smells inherently signify flawed design structures that lead to future bugs and errors. It increases the software budget and eventually makes the system hard to maintain or completely obsolete. To mitigate these challenges, practitioners must detect and refactor code smells. However, the theoretical interpretation of smell definitions and intelligent establishment of threshold values pose a significant conundrum. Supervised machine learning emerges as a potent strategy to address these problems and alleviate the dependence on expert intervention. The learning mechanism of these algorithms can be refined through data pre-processing and hyperparameter tuning. Selecting the best values for hyperparameters can be tedious and requires an expert. This study introduces an innovative paradigm that fuses twelve swarm-based, meta-heuristic algorithms with two machine learning classifiers, optimizing their hyperparameters, eliminating the need for an expert, and automating the entire code smell detection process. Through this synergistic approach, the highest post-optimization accuracy, precision, recall, F-measure, and ROC-AUC values are 99.09%, 99.20%, 99.09%, 98.06%, and 100%, respectively. The most remarkable upsurge is 35.9% in accuracy, 53.79% in precision, 35.90% in recall, 44.73% in F-measure, and 36.28% in ROC-AUC. Artificial Bee Colony, Grey Wolf, and Salp Swarm Optimizer are the top-performing swarm-intelligent algorithms. God and Data Class are the most readily detectable smells with optimized classifiers. Statistical tests underscore the profound impact of employing swarm-based algorithms to optimize machine learning classifiers, corroborated by statistical tests. This seamless integration enhances classifier performance, automates code smell detection, and offers a robust solution to a persistent software engineering challenge.

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

Metaheuristics exposed: Unmasking the design pitfalls of arithmetic optimization algorithm in benchmarking

This work unveils design flaws within most metaheuristics, with a specific focus on issues associated with the arithmetic optimization algorithm (AOA). Despite being a simple metaheuristic optimizer inspired by mathematical operations, AOA holds promise for addressing complex real-world applications. However, a thorough analysis of its search mechanism reveals a heavy dependence on problem bounds for the quality of solutions obtained by AOA. Additionally, discrepancies between algorithm descriptions and implementations in AOA can mislead users and impede progress within the metaheuristic community. Experimental simulations conducted on various standard benchmarks including their shifted versions indicate a structural bias in AOA, leading to artificially high accuracy in fitting standard test functions but poor performance when applied to shifted benchmarks. Finally, we give a critical cause analysis and conclude this article by highlighting valuable research avenues in this field.

Thermal efficiency and performance analysis of 50 MW concentrated solar power plant

This study evaluates the operational efficiency and performance of the Shagaya 50 MW Concentrated Solar Power (CSP) plant in Kuwait that has been operational since February 2019. Utilizing Parabolic Trough technology, the plant incorporates a large Solar Field (SF) comprising 8 platforms with total of 206 solar-collector loops. Thermal energy captured by the SF is utilized in a Low Temperature Rankine Cycle to generate electricity via water-steam cycle and heat exchangers, with a nominal turbine capacity of 50 MW. Supported by a thermal energy storage (TES) system capable of storing 1200 MWht energy in molten salt. Through a comprehensive assessment spanning 41 months, real-time operational data is analysed to evaluate the CSP-TES system's efficacy within Kuwait's climatic conditions. Various performance parameters are examined, providing insights into CSP technology's practical implications and operational strategies. Driven by the annual difference in downtime and TES availability, comparative analysis of the plant's annual electricity production over the three years reveals notable variations: 74.99 GWh in 2019, 160.62 GWh in 2020, and 140.04 GWh in 2021, corresponding to capacity factors of 19 %, 40.75 %, and 35.53 %, respectively. The findings underscore the strong correlation between the SF and TES system in determining overall plant efficiency. The study also highlights the impact of high wind speeds on plant operation, triggering shutdowns on 9 days during the assessment period. Additionally, soiling losses in the SF are assessed through daily reflectivity measurements, guiding cleaning efforts using semi-automated armed trucks. Seasonal peaks in cleanliness factor coincide with local spring season, underlining the detrimental effect of reflectivity losses on plant output due to higher dust mass densities. A design flaw causing frequent breakdowns of receiver tubes under high winds is identified and addressed.

Post‐tension retrofitting of RC dapped‐end beams: A numerical investigation

AbstractDapped‐end beams are frequently used in roofing and flooring systems of precast reinforced concrete buildings as well as in bridge constructions. Due to design flaws, deterioration, and construction mistakes, they may have unsatisfactory structural performance. Past experimental studies investigated the effectiveness of post‐tension strengthening techniques applied to dapped‐end beams, revealing them as effective solutions, even though a limited range of influencing parameters were considered. This paper numerically investigates the performance improvement provided by post‐tension interventions applied to dapped‐end beams. Numerical models were built through a refined FEM software simulating two experimental tests found in the literature. After the models' calibration, a post‐tension intervention was designed and implemented in the models, considering the level of prestressing as the main intervention's parameter. Afterwards, the behavior in the post‐intervention condition was analyzed to find changes in the failure modes and highlight the performance improvement concerning cracking phenomena and ultimate load‐bearing capacity. It is found that the level of tendons' prestress significantly improves the cracking load, which increases almost linearly with respect to it. Conversely, the significant gain in terms of ultimate load‐bearing capacity (up to 53%) shows only slight variations when the prestress level changes. Finally, accounting also for the reduction of deformation capacity for high prestressing levels, practical suggestions are provided regarding the optimal post‐tension choice.

Improving Real-Time Performance of Micro-ROS with Priority-Driven Chain-Aware Scheduling

Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact the real-time performance of micro-ROS. To improve the timing predictability and run-time efficiency of micro-ROS, we design and implement a priority-driven chain-aware scheduling system (PoDS) based on the existing micro-ROS architecture. The experimental results demonstrate that our proposed PoDS exhibits significantly improved real-time performance compared to the default micro-ROS.

Research on Performance Evaluation of Sponge City PPP Projects in the Yangtze River Delta Region

In order to alleviate urban water ecological problems and promote sustainable urban development, many cities in my country are building sponge cities. At present, the country is vigorously promoting the application of the PPP model for project construction. The national government departments are actively preparing for the construction of sponge cities, promoting the construction of sponge cities in 30 pilot cities across the country, and encouraging sponge city construction projects using the PPP model, attracting a large amount of social capital to participate, and promoting The PPP model has been widely used in sponge city construction projects. This article takes the sponge city PPP project as the research object. In order to scientifically and rationally evaluate the construction performance of the sponge city PPP project, and on the basis of studying and summarizing the characteristics of the sponge city PPP project model and the development status of the performance evaluation system in my country, we conduct an analysis of the content of the performance evaluation. and research; establish and select a performance evaluation index system; conduct a comparative analysis of this model with other commonly used models, and build a PSIR-based PPP project performance evaluation model based on the design flaws of the existing performance evaluation system; and analyze and summarize the data. , clarifying its specific application and impact in practice; finally, a summary and outlook of the entire research are given.

The application of hierarchical perception technology based on deep learning in 3D fashion design

In recent years, 3D fashion design has been relied on for improving attire fashion, design, and presentation with fewer flaws and better visualization. This aids consumers in providing visualized recommendations on modifications, suggestions, and customized attire designs. Considering the influence of automation and intelligent processing in the fashion designing industry, it introduces a Flaw Detection Method in 3D Representation (FDM-3DR) to reduce frequent modifications. The proposed method visualizes the design in three dimensions for its completeness and flawless representation. Based on the consumer recommendation, the lack of design flaws in the representation is identified, and multiple detections are presented. This is required to improve consumer satisfaction and the multi-dimensional projection between flaws and complete attire products. The learning is trained using the fixable representation, and therefore, the previous unsuitable designs are repelled by different recommendations. This improves the design adaptability, recommendation ratio, and representation ratio. Besides, it reduces the recommendation time and flaws.

Application of time‐space neighborhood standardization technology to complex multi‐stage process fault detection

AbstractTo solve the problem that the multi‐stage process with dynamicity and nonlinear is hard to monitor effectively, the time‐space neighborhood standardization (TSNS) method is proposed, which is further applied to partial least squares (PLS) to propose TSNS and PLS (TSNS‐PLS) method for process fault detection. TSNS can transform multi‐stage data into single‐stage data that approximately obeys a standard normal distribution, remove temporal correlation between samples at previous and subsequent moments in the process data, and separate online fault samples. TSNS makes the transformed process data satisfy the requirements of the PLS method for process data and can significantly improve the fault detection rate of the PLS method. Finally, the performance of TSNS‐PLS was examined by a numerical simulation process and the penicillin fermentation process design fault detection experiment.

A Team Cognition Measurement Method for Single Pilot Operations Human-Machine System Design

Team cognition is an important indicator to reflect the trend of single pilot operation (SPO) human-machine system team performance. How to measure team cognition to explore different function allocation decisions before the implementation of prototypes or simulators has become a significant step in the early SPO human-machine system design. Therefore, this article proposed a team cognition measurement method based on the social network analysis (SNA) method, namely cognitive social network analysis (CSNA). The CSNA method firstly identified the nodes, edge directions and channel resource values of each task; secondly calculated the cognitive demands of each task based on multiple resource theory; thirdly summed the cognitive demands of the same task to construct the association matrix; and finally calculated global and nodal network metrics. The results of the study showed that (1) the CSNA method could identify design flaws of the initial SPO human-machine system; (2) the CSNA method increased the ability to identify the changes in both the cognitive demands of tasks and the overall workloads of human agents compared to the SNA method; (3) the CSNA method could help researchers make reasonable design suggestions to improve the team performance of the redesigned SPO human-machine system. The above results not only verify the feasibility of the CSNA method, but also show that the CSNA method is superior to the current SNA method.

Innovating an electrostatic charging unit with an insulated induction electrode for air-assisted orchard sprayers

Orchard pesticide applications using electrostatic spraying technology enhance droplet deposition and reduce environmental pollution. However, the incorporation of charging systems into conventional air-assisted sprayers remains limited. This is primarily due to challenges such as the necessity for high air velocity to prevent droplet adherence to electrodes, electrode-nozzle design flaws, and safety concerns related to the high voltage used for charging. To address these issues, this study developed five charging units that employ insulated induction electrodes, eliminating exposure to external elements. These units were incorporated into an air-assisted sprayer to assess their effectiveness in charging spray droplets and improving deposition. The units successfully limited electrical leakage, even under high-voltage conditions (up to 18 kV) and in the absence of airflow. At an electrode voltage of 14 kV, and an air velocity of 27 m s−1, the electrostatic units achieved an average charge-to-mass ratio of up to 0.67 mC kg−1. Activation of the charging units significantly enhanced droplet deposition with an increase of 90% on the front and 3250% on the back surfaces of the artificial targets (aluminum plates). On real plant leaves, the increase was 54% on the front and 112% on the back surfaces compared with uncharged standard spraying.

An intelligent body frame structure modeling and optimization system based on conceptual design

The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.

THE STUDY OF SBS IN BUILDINGS – IMPACT ON HEALTH AND TECHNOLOGICAL SOLUTION

Sick Building Syndrome is a condition whereby occupants of a building experience sicknesses that can be linked to the amount spent in that particular building. The end users experience unhealthy mild or sometimes severe symptoms or fall sick with such conditions as allergies as well as irritations of the throat, eyes, nose and also lethargy and sometimes dizziness Mølhave (1989). Unlike Building-related illnesses (BRI), SBS has no identifiable cause. In 1984, the World Health Organization (WHO) estimated that 30% of new and remodelled buildings worldwide may have SBS. This study will analyze the severity of SBS in North Cyprus based on different building elements and how they support the development of SBS in a building. Part of the outcome of this work is to develop a framework to test the level of SBS in a residential building by testing how occupants react to the building and demonstrating that building design elements such as orientation, roofing insulation systems, roof installations, and thermal floor masses alter the SBS effect on occupants. The goal is to develop "SBS TEST SOFTWARE" to assist professionals in assessing the SBS level in a building utilizing direct interview software, which can readily identify SBS-prone structures in Cyprus and worldwide. Cyprus’ SBS difficulties can be attributed to several design flaws in building elements, improper ventilation, uneven or inadequate heat distribution, and a lack of moisture-proofing of roof decks, walls, and foundations Obi (2016).

An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method.

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool.

Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately

A framework for formal verification of robot kinematics

As robotic applications continue to expand and task complexity increases, the adoption of more advanced and sophisticated control algorithms and models becomes critical. Traditional methods, relying on manual abstraction and modeling to verify these algorithms and models, may not fully encompass all potential design paths, leading to incomplete models, design defects, and increased vulnerability to security risks. The verification of control systems using formal methods is crucial for ensuring the safety of robots. This paper introduces a formal verification framework for robot kinematics implemented in Coq. It constructs a formal proof for the theory of robot motion and control algorithms, specifically focusing on the theory of robot kinematics, which includes the homogeneous representation of robot coordinates and the transformation relations between different coordinate systems. Subsequently, we provide formal definitions and verification for several commonly used structural robots, along with their coordinate transformation algorithms. Finally, we extract the Coq code, convert the functional algorithms into OCaml code, and perform data validation using various examples. It is worth emphasizing that the framework we have built possesses a high level of reusability, providing a solid technological foundation for the development of kinematics theorem libraries.

Security and Privacy Column

Cryptographic protocols are really hard to design: they mix difficulties stemming from distributed systems, rely on cryptographic primitives and need to provide complex guarantees in the presence of an arbitrary attacker running in parallel. Rigorous security proofs are basically the only hope to avoid design flaws as witnessed by numerous examples. This has been recognized since the early 80's and two main approaches have been actively developed since. The computational model is rooted in complexity theory and relies on a reduction of the security of the protocol to a security assumption of a cryptographic primitive which itself may rely on the hardness of a computational problem. Attackers are any probabilistic polynomial time Turing Machines (PPTMs). A security proof in this model provides strong guarantees. However, getting proofs in such a detailed model correct is challenging. The symbolic model takes a different approach: messages are modeled as first-order logic terms and cryptographic primitives such as encryption schemes are idealized. Typically, only the intended functionality of an encryption is modeled by an equation such as dec(enc(m,k),k) = m to state that decryption and encryption cancel out when the same key k is used. Crucially, only the properties that are explicitly stated hold and the adversary actions are restricted to those. At the cost of being more abstract this approach is amenable to automation and tools nowadays succeed in proving industrial protocols, such as TLS or Signal.

Unveiling the truth: is COVID-19 reimbursement in Colombia a flawed design? A cost-of-illness analysis for moderate, severe and critical infections

PurposeThis study examines the financial impact of the COVID-19 pandemic on the Colombian Health System, focusing on the adequacy of reimbursement rates for inpatient stays. The study, based on a...

Automatic Debugging of Design Faults in MapReduce Applications

Automatic Debugging of Design Faults in MapReduce Applications