Taiwan Association of Engineering and Technology Innovation: E-Journals
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Hybrid RSA–SHA-256 Scheme for Enhancing Physical-Layer Security in MIMO-OFDM Systems Using SDR Implementation
Get PDFThis research aims to enhance the physical-layer security (PLS) of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication systems. The proposed method integrates Rivest–Shamir–Adleman (RSA)-based subcarrier index scrambling with secure hash algorithm (SHA)-256 hashing to ensure both confidentiality and integrity without altering the transmitted waveform. The scheme is implemented on a 2×2 Universal Software Radio Peripheral (USRP)-based MIMO-OFDM software-defined radio (SDR) testbed and evaluated using symbol distribution analysis, bit-error-rate (BER) performance, and security assessment. Experimental results indicate that RSA-based scrambling substantially increases symbol randomness while preserving Shannon-consistent BER performance across multiple modulation orders, with minimal penalty in signal-to-noise ratio (SNR). Additionally, security analysis demonstrates that the combination of RSA-2048 and SHA-256 is resistant to Wiener’s attack and the common modulus attack, thereby supporting efficient and secure real-time MIMO-OFDM communication
Buckling and Strength of Cold-Formed Thin-Walled Built-Up Columns under Axial Compression: Tests and Code Evaluation
Get PDFThis study examines the axial-compression behaviour of cold-formed thin-walled steel built-up columns. Sixty concentric axial tests are carried out on six cross-section configurations (single C- and U-sections and four screw-fastened built-up sections) with lengths from 500 to 3000 mm. The measured capacities are compared with predictions from the Chinese code GB 50018-2002 using the Effective Width Method (EWM) and from AISI S100-2007 using both EWM and the Direct Strength Method (DSM). For closed multi-limb built-up sections, members with slenderness λ < 70 failed mainly by local buckling, whereas λ > 70 led to global buckling. For open built-up sections, λ < 80 is governed by distortional buckling and λ > 80 by global buckling. Overall, GB EWM provided the closest strength predictions for the built-up members, while AISI DSM is generally conservative. The results offer practical guidance for the design of cold-formed steel built-up compression members
Real-Time Code Vulnerability Detection Using a Machine Learning-Integrated Language Server
Get PDFThe rapid growth of software development has improved productivity but also introduced security risks, especially when developers skip essential scans due to time constraints or limited tool support. This study proposes a real-time vulnerability detection system that integrates machine learning (ML) into a language server framework to enhance software security during coding. The system uses a Language Server Protocol (LSP) architecture with a Random Forest classifier that analyzes source code at the line level. Code is pre-processed through tokenization, abstract syntax tree (AST) traversal, and TF-IDF vectorization before being classified into four vulnerability types: CWE-79 (Cross-Site Scripting), CWE-89 (SQL Injection), CWE-22 (Path Traversal), and CWE-434 (Unrestricted File Upload). Using 20,000 labeled code lines, the model achieves 82.3% accuracy and an F1-score of 80.7%, performing best on CWE-79 and CWE-89 and showing weakest performance on CWE-434. The language server averages 72 ms per diagnostic, demonstrating its suitability for real-time developer workflows
A Hybrid PMU-PDC-Cloud IoT Architecture for Enhanced Power Grid Monitoring
Get PDFMonitoring power grids is critical to maintaining their reliability and stability, especially with the increasing integration of distributed energy resources (DERs). This study aims to develop a smart and scalable power grid monitoring system. The proposed system integrates phasor measurement units (PMUs), a phasor data concentrator (PDC), and a cloud-based Internet of Things (IoT) platform to achieve continuous monitoring and analysis. The system enables simultaneous measurements, real-time visualization, and predictive analytics using advanced frequent tracking algorithms. The cloud infrastructure enables real-time data visualization. Experimental evaluation demonstrates that the system achieves high sensitivity in fault detection, accurately identifying voltage variations as small as 0.01 pu based on system nominal voltage, phase angle deviations within ±5°, and frequency anomalies. This enhances proactive fault detection and reduces service interruptions
Eco-Pave: Paving Blocks Originating from Construction Waste
Get PDFConstruction waste is a significant contributor to global solid waste, underscoring the need for effective sustainable management strategies. This study aims to assess the quality and environmental impact of paving blocks manufactured from two different sizes of recycled aggregates. Two categories, CA1 (12.5–4.75 mm) and CA2 (37.5–4.75 mm), were used as constituent materials. The paving blocks were assessed based on compressive strength, water absorption, and wear resistance. Experimental results indicate that paving blocks incorporating CA2 recycled aggregates performed better than those with CA1. The tested paving blocks meet grade D standards for garden paving (CA1) and grade C standards for pedestrian pathways (CA2). Additionally, utilizing recycled aggregates from concrete waste enables 48.1% rubble recycling and reduces carbon dioxide emissions by 64.7%, thereby contributing to sustainable waste management
Power Generation Optimization of Reverse Electrodialysis: Effects of Key Operating and Design Parameters
Get PDFSalinity gradient power (SGP) via reverse electrodialysis (RED) is a promising renewable energy source; however, maximizing efficiency requires balancing complex electrochemical and hydraulic variables. This study systematically investigates the effects of key operating and design parameters on RED power generation to improve energy efficiency. Laboratory-scale RED experiments are conducted by varying the concentrations of high-concentration (HC) and low-concentration (LC) NaCl solutions, space velocity, the number of ion-exchange membrane (IEM) pairs, and flow channel thickness. The results show that power density increases with concentration ratio and absolute solution concentration, with optimal performance achieved at HC concentrations of 1-2.5 M, LC concentrations of 0.02-0.05 M, and a concentration ratio above 50:1. An optimal space velocity of 0.25 1/min is identified. Increasing the number of IEM pairs enhances power density, while flow channel thicknesses of 1-2 mm minimize resistance. Under optimized conditions, a maximum power density of 0.446 W per square meter is achieved
Model Tests on Lateral Bearing Behaviors of Cone-shaped Hollow Flexible Reinforced Concrete Foundation in Coarse Sand under Monotonic Lateral Load
Get PDFWith the rapid increase in installed onshore wind turbine capacity, greater lateral bearing capacity of foundations is required. The cone-shaped hollow flexible reinforced concrete foundation (CHFRF) is proposed. Model tests and numerical simulations are conducted to investigate the lateral bearing behavior of the CHFRF in coarse sand. The results indicate that the lateral bearing capacity of CHFRF increases with the aspect ratio and decreases with loading eccentricity. Compared with a circular foundation with the same height, the CHFRF with a diameter of 400 mm exhibits an average increase of 17.6% in lateral bearing capacity while using only 65% of the concrete volume. The rotation center is located at 0.5–0.65 times the foundation height. Based on the earth pressure distribution at the limit state and the limit equilibrium method, a theoretical approach for predicting the CHFRF lateral bearing capacity is proposed
Influence of Electrode Spacing on Grounding Resistances in Electrical Networks for Effective Lightning Protection
Get PDFThis study aims to analyze the influence of inter-electrode spacing on grounding resistance in high-voltage transmission networks. A simplified analytical model is applied to a case study on the Djiri-Ngo 220 kV line (Republic of the Congo), considering two representative soil types: clayey and siliceous sand. The grounding resistance is calculated by varying the number of electrodes and their spacing. The results show that increasing electrode spacing reduces grounding resistance. In certain configurations, the improvement exceeding 50 % when the spacing is increased from 5 m to 25 m. A saturation threshold is identified, beyond which further increases in spacing yields diminishing returns. Electrode spacing proves to be a key design factor, sometimes more influential than the number of electrodes. The proposed parametric geometric analysis offers a practical and cost-effective strategy for grounding system design, emphasizing the importance of adapting configurations to local geotechnical conditions
Uplift Capacity and Displacement of Pre-Bored PC Piles in Undrained Soils
Get PDFThis study aims to present interpretation recommendations to aid in the design of pre-bored PC piles in undrained soils, along with preliminary results for model factors to characterize model uncertainty. Measured capacities are estimated from the load-displacement curves using six interpretation methods, each assessed by employing the L2 method as a normalizing criterion. Predicted capacities are calculated via the α and β methods using analysis parameters for drilled shafts and are compared with measured capacities. Results show mean normalized capacities ranging from 0.45 to 1.60, with 6.4 to >43.9 mm displacements. A comparison of load-displacement behaviors reveals that pre-bored PC piles require greater displacements to mobilize the capacity than drilled shafts, driven piles, and barrette piles. The analysis highlights an overprediction of side resistance. Model factors, defined as the ratio of measured to predicted capacities, can calibrate resistance factors for reliability-based design
Multiclass Plant Leaf Disease Prediction Using Fuzzy Multimodal Feature Extraction
Get PDFDelayed identification of crop diseases, which significantly impact agricultural yields, remains a critical challenge. Crop diseases are a major factor contributing to reducing productivity. Since leaves are the mirrors of crop health, by investigating the leaves, a prediction of crop health can be made. This study aims to predict crop disease in the vegetative growth phase with greater efficiency. The two most prominent features, color and texture of the leaves, are extracted with different techniques, followed by fuzzification of these features. Two machine learning models, the bootstrap model and the multi-class support vector machine (MSVM), are employed for disease prediction. The findings show that for multi-class disease prediction, the bootstrap model with histogram and modified co-occurrence matrix features obtains a superior average accuracy of 98.07%, while the MSVM with fuzzy features delivers an average accuracy of 80.11% in the potato crop with early blight disease