Multidisciplinary Journal of Engineering and Technology
type: channel
description: Multidisciplinary Journal of Engineering and Technology (MJET) is an international peer-reviewed, open access journal.
publisher: UPub Science
language: en
publication name: Multidisciplinary Journal of Engineering and Technology
issn: 3007-6897
items: DESIGN AND IMPLEMENTATION OF AN EXAMINATION ANTI-CHEATING SYSTEM IN COMPUTER LABORATORIES BASED ON LIGHTWEIGHT COMPUTER VISION
items: IMPLEMENTATION OF A VIOLATION BEHAVIOR IDENTIFICATION AND EARLY WARNING SYSTEM FOR INDUSTRIAL WORKSHOPS
items: THE HIGH AND LOW TEMPERATURE TEST SCHEME OF DC-DC CONVERTERS
items: EXPERIMENTAL TEST OF COMBUSTION AND EXPLOSION PRESSURE OF METHANE-AIR PREMIXED GAS
items: CONSTRUCTION TECHNIQUES AND APPLICATIONS FOR CRACK PREVENTION IN CANTILEVERED MULTI-STORY LARGE-SPAN STEEL-REINFORCED CONCRETE WALLS
items: MAPPING CULTURAL ART GENES: A VISUAL GENEALOGY FRAMEWORK FOR CULTURAL REPRESENTATION AND TRANSLATION
items: RESOURCE SCHEDULING STRATEGY FOR INTERNET OF THINGS EDGE COMPUTING BASED ON ARTIFICIAL POTENTIAL FIELD ALGORITHM
items: THERMAL EFFECTS ON THE LD END-FACE PUMPED ND:YAG LASER MEDIUM
items: DESIGN OF TRAM ALGORITHM BASED ON GRAPH TRANSFORMATION
items: AI FOR CREDIT RISK MODELING: A DEEP LEARNING APPROACH
items: A BIOMIMETIC BAT FLAPPING VEHICLE DESIGN FOR ENVIRONMENTAL MONITORING APPLICATIONS
items: UAV ATTITUDE AND ALTITUDE STABILITY CONTROL ALGORITHM UNDER EXTREME WEATHER CONDITIONS
items: A HYBRID GENETIC-GRADIENT DESCENT ALGORITHM FOR OPTIMAL ELECTRIC VEHICLE CHARGING STATION PLACEMENT
items: EXPLORATION OF INDUSTRY-EDUCATION INTEGRATION MODEL FOR MEDICAL-ENGINEERING TALENT DEVELOPMENT IN THE CONTEXT OF DIGITAL LIVELIHOOD
items: ENERGY AND MAGNETIC FIELD DISTRIBUTION CHARACTERISTICS OF PM AND JONSWAP SPECTRA
items: CONSTRUCTION TECHNOLOGY AND APPLICATION OF FRAME COLUMNS SUBJECTED TO ALTERNATING TENSION AND COMPRESSION FORCES USING THE REVERSE CONSTRUCTION METHOD
items: ANALYSIS, COMPARISON AND APPLICATION OF DRY AND WET DRILLING METHODS FOR ANTI-BUOYANCY ANCHOR BOLTS
items: INTEGRATING INDIGENOUS KNOWLEDGE SYSTEMS INTO ADVANCED MATHEMATICAL FORMULA DEVELOPMENT: A FRAMEWORK FOR CURRICULUM INNOVATION IN SOUTHERN AFRICA
items: CONSTRUCTION TECHNOLOGY OF LARGE-SPAN CAVITY VENTILATION SHAFT WITH SMALL CLEARANCE ON ROOF
items: DIGITAL FORENSIC INVESTIGATION IN CYBERCRIME CASES: CASE STUDIES AND RECOMMENDATIONS
items: A COMMAND SUPPORT MODEL BASED ON BLOCKCHAIN AND THE METAVERSE
items: ENHANCED UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION USING CONTRASTIVE LEARNING AND HISTOGRAM OF ORIENTED GRADIENTS
items: STUDY ON EQUIVALENT STATIC WIND LOAD ON LONG-SPAN ROOF BASED ON RESPONSE-RELATED SCALE
items: A REINFORCEMENT LEARNING FRAMEWORK FOR ACCURATE AND CONTEXT-AWARE LEGAL DOCUMENT SUMMARIZATION
items: A KNOWLEDGE-DRIVEN FRAMEWORK FOR ENHANCING LEGAL DECISION SUPPORT WITH LARGE LANGUAGE MODELS
items: LEGAL RECOMMENDATION SYSTEMS: APPLICATIONS, TECHNOLOGIES, AND FUTURE DIRECTIONS IN THE DIGITAL AGE
items: DEVELOPMENT AND APPLICATION OF ELECTRICAL ENGINEERING IN ARTIFICIAL INTELLIGENCE AND BIG DATA ENVIRONMENT
items: SATELLITES SYSTEMS AND NIGERIAN COMMUNICATION SATELLITES (NIGCOMSAT): AN OVERVIEW
items: PERFORMANCE COMPARISON OF CEEMDAN-LSTM AND BASIC LSTM MODELS IN PREDICTING REALIZED VOLATILITY
items: EARLY BEARING FAULT DETECTION AND RECOGNITION METHOD BASED ON INSTANCE TRANSFER

DESIGN AND IMPLEMENTATION OF AN EXAMINATION ANTI-CHEATING SYSTEM IN COMPUTER LABORATORIES BASED ON LIGHTWEIGHT COMPUTER VISION
type: item
description: With the deep advancement of educational informatization, computer-based examinations in university computer laboratories have become a critical approach for evaluating students' practical skills. However, traditional centralized video invigilation systems impose severe computation and network bandwidth strains, making them impractical for legacy hardware deployments. To address these bottlenecks, this paper designs and implements a lightweight, multi-dimensional behavior auditing invigilation system based on decentralized edge computing. By offloading image capturing, posture estimation, and decision-making workflows entirely onto the localized client side, the proposed system restricts network transmissions to low-frequency, structured text logs, fundamentally eliminating the risk of campus network congestion caused by concurrent high-definition video streaming. Specifically, on the algorithmic layer, the system leverages the native features of the 44 homogeneous transformation matrix from the WebAssembly-accelerated MediaPipe Tasks-Vision framework. It performs inverse kinematics for head yaw and pitch via basic scalar operations in the JavaScript runtime, completely bypassing heavy dependencies such as OpenCV.js. Concurrently, a geometric mathematical model utilizing Euclidean distance ratios of iris and pupil landmarks is established for eye gaze tracking. Furthermore, a dual-layer cooperative anti-false-alarm mechanism integrating spatial directional branching and a temporal sliding window filter is proposed, which smoothly filters out transient physiological noises and benign gestures. Experimental results on a simulated low-end host demonstrate that the system achieves an exceptionally low single-frame computation latency of 0.62 ms and restricts the total memory footprint to 146.5 MB. Under steady-state operations, it reduces the false positive rate (FPR) of environmental and physiological noise to 1.3% while maintaining a high true positive rate (TPR) of 9…
subject: Research Article
  topic: section
subject: Lightweight computer vision
  topic: keywords
subject: Behavior auditing
  topic: keywords
subject: Face landmarker
  topic: keywords
subject: Sliding window filter
  topic: keywords
creator: ZiRui Wu
creator: FanHao Ye
creator: ZhenNan Zhang
creator: LuTing Wu
creator: XinYin Lan
creator: MuCong Chi
rights: Copyright (c) 2026 ZiRui Wu; FanHao Ye; ZhenNan Zhang; LuTing Wu; XinYin Lan; MuCong Chi https://creativecommons.org/licenses/by/4.0
date: 2026-06-06
publication date: 2026-06-06
starting page: 7
ending page: 13
doi: 10.61784/mjet3039

IMPLEMENTATION OF A VIOLATION BEHAVIOR IDENTIFICATION AND EARLY WARNING SYSTEM FOR INDUSTRIAL WORKSHOPS
type: item
description: Aiming at the pain points in industrial workshops, such as the low efficiency of manual monitoring, poor computing power adaptability of existing vision-based systems, and the lack of a closed-loop alarm mechanism, this paper designs and implements a violation behavior identification and early warning system. First, based on the YOLOv8n model developed by the Ultralytics team, the system incorporates depthwise separable convolutions and attention mechanisms for lightweight optimization. Combined with TensorRT quantization and compression, this ensures low-latency deployment on edge devices. Second, a four-layer architecture is constructed, comprising the Perception Layer, Edge Computing Layer, Intelligent Decision Layer, and Application Layer. The introduction of an intelligent decision mechanism enables an end-to-end automated closed-loop of identification-alarm-archiving. The system is integrated with DingTalk/WeCom and MES (Manufacturing Execution System) to facilitate real-time alarm notifications and data logging. Finally, the system's performance was validated through experiments in real-world industrial workshop scenarios. The results indicate that the average inference latency at the edge is 39 ms, the identification accuracy for violations reaches 92.3%, and the false alarm rate is reduced to 3.2%. All metrics meet the real-time monitoring requirements of industrial sites.
subject: Research Article
  topic: section
subject: Industrial workshop
  topic: keywords
subject: Violation identification
  topic: keywords
subject: Edge computing
  topic: keywords
subject: YOLO
  topic: keywords
subject: TensorRT quantization
  topic: keywords
creator: ZiRui Wu
creator: XinYin Lan
creator: FanHao Ye
creator: ZhenNan Zhang
creator: MuCong Chi
rights: Copyright (c) 2026 ZiRui Wu; XinYin Lan; FanHao Ye; ZhenNan Zhang; MuCong Chi https://creativecommons.org/licenses/by/4.0
date: 2026-05-21
publication date: 2026-05-21
starting page: 1
ending page: 6
doi: 10.61784/mjet3038

THE HIGH AND LOW TEMPERATURE TEST SCHEME OF DC-DC CONVERTERS
type: item
description: This paper presents an optimized high-low temperature test scheme for DC-DC converters, addressing the challenges of test stability, efficiency, and temperature control. The proposed scheme, utilizing an Automated Test Equipment (ATE) system and a thermal shield, significantly improves upon the original testing method. It effectively mitigates test stability issues, enables rapid temperature changes, and completes single temperature testing in one step, thereby enhancing test efficiency. Moreover, it provides precise temperature control during testing, avoiding influences from the circuit’s heat and environmental temperature. The results validate the superiority of the optimized scheme over the original, marking a significant advancement in the high-low temperature testing of DC-DC converters. This study contributes to the ongoing efforts to improve the reliability and performance of DC-DC converters, which are pivotal in various sectors. Future work could explore further enhancements to the testing scheme and its applicability to other electronic devices.
subject: Research Article
  topic: section
subject: DC-DC converters
  topic: keywords
subject: High-low temperature test
  topic: keywords
subject: Automated Test Equipment (ATE)
  topic: keywords
creator: ZhiWei Ma
rights: Copyright (c) 2026 ZhiWei Ma https://creativecommons.org/licenses/by/4.0
date: 2026-04-07
publication date: 2026-04-07
starting page: 31
ending page: 35
doi: 10.61784/mjet3037

EXPERIMENTAL TEST OF COMBUSTION AND EXPLOSION PRESSURE OF METHANE-AIR PREMIXED GAS
type: item
description: Methane, as a clean energy source, is increasingly widely used in industrial fields. The combustion and explosion characteristics of its premixed gas are crucial for efficient energy utilization and industrial safety. This paper presents experimental tests on the combustion and explosion of methane-air premixed gas. A 1000cc volume explosion device was designed and fabricated, and methane and air were sequentially filled into it. Four sets of combustion and explosion experiments were performed using methane–air premixed gas at different equivalence ratios, and the pressure variations throughout the entire combustion and explosion process were recorded. The results show that the peak pressure of the methane-air premixed gas combustion and explosion reaches 8-9 times the original premixed gas pressure. The experimental results provide important data support for the industrial application design of methane.
subject: Research Article
  topic: section
subject: Methane
  topic: keywords
subject: Explosion device
  topic: keywords
subject: Combustion and explosion experiment
  topic: keywords
subject: Premixed gas
  topic: keywords
creator: XianRen Zeng
creator: Ke Yang
creator: QingHeng Zeng
creator: LinMei Li
creator: ZhiYue Chen
rights: Copyright (c) 2026 XianRen Zeng; Ke Yang; QingHeng Zeng; LinMei Li; ZhiYue Chen https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 26
ending page: 30
doi: 10.61784/mjet3036

CONSTRUCTION TECHNIQUES AND APPLICATIONS FOR CRACK PREVENTION IN CANTILEVERED MULTI-STORY LARGE-SPAN STEEL-REINFORCED CONCRETE WALLS
type: item
description: This paper proposes a crack-prevention construction method for cantilevered multi-story, large-span steel-reinforced concrete walls, based on a specific multi-story structure with a 23m span. Before construction, the method involves building a three-dimensional finite element numerical model to thoroughly analyze the stress distribution of the cantilevered multi-story steel-reinforced concrete exterior wall under various working conditions. This allows for the optimization of reasonable construction procedures at each stage and the division of the cantilever structure into tension and compression zones. After the multi-story cantilevered steel frame is assembled, the temporary support system at the bottom is removed. The compression zone and wall sections subjected to smaller loads are then cast in a stepped manner, followed by the tension zone. This increases the overall stiffness of the cantilever wall in the compression zone and releases stress in the tension zone to mitigate strain. This cleverly avoids simultaneous deformation of the concrete and steel frame due to excessive tensile stress, thereby reducing the risk of large-area cracks or through-cracks in the cantilever wall.
subject: Research Article
  topic: section
subject: Cantilevered
  topic: keywords
subject: Multi-story large-span
  topic: keywords
subject: Steel-reinforced concrete wall
  topic: keywords
subject: Crack-prevention construction
  topic: keywords
creator: GuangLiang Qiu
creator: Jie Luo
creator: XiaoXia Zhao
creator: BoBo Li
creator: BaoAn Zhang
rights: Copyright (c) 2026 GuangLiang Qiu; Jie Luo; XiaoXia Zhao; BoBo Li; BaoAn Zhang https://creativecommons.org/licenses/by/4.0
date: 2026-01-26
publication date: 2026-01-26
starting page: 13
ending page: 19
doi: 10.61784/mjet3034

MAPPING CULTURAL ART GENES: A VISUAL GENEALOGY FRAMEWORK FOR CULTURAL REPRESENTATION AND TRANSLATION
type: item
description: This article proposes a visual genealogy framework for mapping cultural art genes, reconceptualizing cultural representation as a structured and transferable visual system. Cultural art genes are understood as dynamic units shaped through historical accumulation, aesthetic conventions, and symbolic articulation. By emphasizing genealogical relationships among visual elements, the framework explains how cultural meanings are encoded, reorganized, and translated across media contexts. Visual representation is thus approached as a process of translation rather than static depiction. The proposed framework shifts analytical focus from surface aesthetics to structural relations, offering a theoretical foundation for systematic analysis of cultural representation and visual translation across diverse visual practices.
subject: Research Article
  topic: section
subject: Cultural art genes
  topic: keywords
subject: Visual genealogy
  topic: keywords
subject: Cultural representation
  topic: keywords
subject: Visual translation
  topic: keywords
subject: Gene mapping
  topic: keywords
creator: WeiXin Zhu
creator: Lan Liu
rights: Copyright (c) 2026 WeiXin Zhu; Lan Liu https://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
starting page: 20
ending page: 25
doi: 10.61784/mjet3035

RESOURCE SCHEDULING STRATEGY FOR INTERNET OF THINGS EDGE COMPUTING BASED ON ARTIFICIAL POTENTIAL FIELD ALGORITHM
type: item
description: With the rapid development of the mobile Internet, smart devices are playing an increasingly important role in people's lives. Edge computing is a key technology for solving resource scheduling problems and is of great significance for meeting high-performance user requirements. This paper deeply studies the resource scheduling strategy for Internet of Things edge computing based on artificial potential field algorithm. It analyzes the architecture of edge computing, resource scheduling methods, research problems, and technical methods, and discusses its application scenarios and challenges. Through the introduction of artificial potential field algorithm, the edge computing resource scheduling is optimized to improve system performance and user experience.
subject: Research Article
  topic: section
subject: User experience
  topic: keywords
subject: System performance
  topic: keywords
subject: Internet of things
  topic: keywords
subject: Key technology
  topic: keywords
creator: ZongYang Du
rights: Copyright (c) 2025 ZongYang Du https://creativecommons.org/licenses/by/4.0
date: 2025-01-23
publication date: 2025-01-23
starting page: 29
ending page: 34
doi: 10.61784/mjet3020

THERMAL EFFECTS ON THE LD END-FACE PUMPED ND:YAG LASER MEDIUM
type: item
description: Solid-state lasers are widely used in various industries due to their high stability, compact structure, and excellent beam quality. However, thermal effects have long been a significant constraint on improving laser performance. Thermal effects are primarily caused by the heat deposition resulting from the absorption of pump energy by the laser medium in solid-state lasers. Studying the heat conduction equation of the laser medium is crucial for understanding thermal effects. This paper provides a detailed theoretical derivation for calculating the heat conduction equation of the laser medium in solid-state lasers. A series expansion is used to derive the analytical expression for the heat conduction equation, and the fourth-order Runge-Kutta method is employed to numerically solve it. The temperature simulation results of the laser medium end face using both methods are compared to verify the correctness of the analytical derivation process and to analyze the causes of any errors.
subject: Research Article
  topic: section
subject: Heat conduction equation
  topic: keywords
subject: Power series
  topic: keywords
subject: Runge-Kutta
  topic: keywords
subject: Temperature simulation
  topic: keywords
creator: XingYu Liu
creator: JianSheng Zhang
creator: Wen Du
rights: Copyright (c) 2025 XingYu Liu; JianSheng Zhang; Wen Du https://creativecommons.org/licenses/by/4.0
date: 2025-02-27
publication date: 2025-02-27
starting page: 35
ending page: 43
doi: 10.61784/mjet3021

DESIGN OF TRAM ALGORITHM BASED ON GRAPH TRANSFORMATION
type: item
description: In this study, the TRAM (Trigonometric Reduction and Acceleration for Matrices) algorithm, based on small-angle approximation, table lookup, and matrix compression, is proposed to address the arithmetic bottleneck and real-time constraints of the ARM Cortex-M4 processor in laser graphics processing. The small-angle approximation replaces the generalized trigonometric function on floating point; the double-indexed table is designed to reduce the trigonometric function storage; and the matrix compression strategy is proposed to reduce the computational complexity. The experimental results show that the computation time of the algorithm for a single graphical transformation is significantly shorter than that of the traditional table lookup method and a single hardware acceleration strategy under a 240MHz main frequency. At the same time, the computation error is always kept lower than 0.035% so that the scanning system can realize a higher laser scanning frame rate to meet the real-time requirement of industrial laser dynamic projection and provide a reusable methodological framework for the real-time graphical processing of the resource-constrained embedded system. It provides a reusable methodological framework for real-time graphics processing in resource-constrained embedded systems, which is of great applied value.
subject: Research Article
  topic: section
subject: Graphics transform
  topic: keywords
subject: ARM
  topic: keywords
subject: Algorithm design
  topic: keywords
subject: DMX512 protocol
  topic: keywords
creator: Ting Sun
creator: ZhiHua Hu
rights: Copyright (c) 2025 Ting Sun; ZhiHua Hu https://creativecommons.org/licenses/by/4.0
date: 2025-03-12
publication date: 2025-03-12
starting page: 44
ending page: 48
doi: 10.61784/mjet3022

AI FOR CREDIT RISK MODELING: A DEEP LEARNING APPROACH
type: item
description: Credit risk modeling is a critical component of financial decision-making, enabling lenders to assess the probability of default and optimize credit allocation. Traditional credit scoring models, including logistic regression and decision tree-based classifiers, have limitations in handling non-linear financial relationships, class imbalance, and borrower heterogeneity. Recent advances in artificial intelligence (AI) and deep learning (DL) have introduced more sophisticated models capable of capturing complex borrower patterns while improving risk assessment accuracy. However, AI-driven credit risk models must address key challenges, including class imbalance, fairness, model interpretability, and scalability in real-world financial environments. This study proposes a comprehensive DL-based credit risk modeling framework that integrates graph neural networks (GNNs), generative adversarial networks (GANs), and adversarial fairness learning to enhance credit risk prediction accuracy, fairness, and adaptability across borrower segments. The model leverages autoencoders for feature extraction, cost-sensitive learning for imbalanced classification, and domain adaptation techniques for improved model robustness. Additionally, an explainability layer is incorporated to enhance transparency in credit decision-making. Experiments on real-world credit datasets demonstrate that the proposed framework outperforms traditional credit risk models, achieving higher recall for defaulters, reduced bias in loan approvals, and improved computational efficiency. The findings highlight the potential of AI-driven credit risk modeling to transform risk assessment strategies, ensuring more accurate, fair, and scalable credit allocation for financial institutions.
subject: Research Article
  topic: section
subject: AI-driven credit risk
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Fairness in credit scoring
  topic: keywords
subject: Generative models
  topic: keywords
subject: Graph neural networks
  topic: keywords
subject: Model interpretability
  topic: keywords
creator: Kaoru Fujisawa
creator: Renji Takeda
creator: Aiko Mori
rights: Copyright (c) 2025 Kaoru Fujisawa; Renji Takeda; Aiko Mori https://creativecommons.org/licenses/by/4.0
date: 2025-03-14
publication date: 2025-03-14
starting page: 49
ending page: 56
doi: 10.61784/mjet3023

A BIOMIMETIC BAT FLAPPING VEHICLE DESIGN FOR ENVIRONMENTAL MONITORING APPLICATIONS
type: item
description: This paper presents the design of a bionic bat aircraft based on the flight characteristics of the Australian grey-headed flying fox. By analyzing the wing morphology changes and motion patterns of the grey-headed flying fox, combined with key factors affecting lift, a flapping-wing aircraft was developed. To avoid excessive structural complexity and weight, the biological structure was simplified into four main components: an active forelimb deformation mechanism, an active leg deformation mechanism, a wing flapping mechanism, and the main fuselage. The flapping mechanism employs a planar linkage driven by a single motor to reduce air resistance and manufacturing complexity, while the wing extension and retraction mechanism uses a crank-rocker mechanism to achieve passive motion, simulating biological characteristics and leveraging flapping inertia. This design balances bionic effectiveness with engineering practicality, providing a reference for research on bionic aircraft. Future improvements could focus on lightweight design and flight stability to expand its application prospects.
subject: Research Article
  topic: section
subject: Bionic bat aircraft
  topic: keywords
subject: Flying fox
  topic: keywords
subject: Wing flapping mechanism
  topic: keywords
subject: Crank-rocker mechanism
  topic: keywords
subject: Flight characteristics
  topic: keywords
creator: ZhiWen Hou
creator: Yang Xiang
rights: Copyright (c) 2025 ZhiWen Hou; Yang Xiang https://creativecommons.org/licenses/by/4.0
date: 2025-03-27
publication date: 2025-03-27
starting page: 57
ending page: 60
doi: 10.61784/mjet3024

UAV ATTITUDE AND ALTITUDE STABILITY CONTROL ALGORITHM UNDER EXTREME WEATHER CONDITIONS
type: item
description: Extreme weather conditions pose significant challenges to the stability of attitude and altitude control of uncrewed aerial vehicles (UAVs). Traditional control methods often have problems with response lag and reduced accuracy in intense disturbance environments. This paper proposes a hybrid control framework that integrates disturbance observers and deep reinforcement learning strategies to improve the autonomous control capabilities of UAVs under complex meteorological disturbances. This method models the disturbance trend in real time by extending the state observer. It uses the policy network to dynamically adjust the control output according to the disturbance estimation, thus realizing the closed-loop optimization of perception making. In simulation experiments, the proposed method shows excellent control performance under multiple typical disturbance conditions such as crosswind, gusts, downdrafts, and their combinations. Compared with traditional PID, LQR, and MPC controllers, it significantly improves trajectory stability, control accuracy, and energy consumption. The results show that this study provides a practical and feasible new idea for robust UAV control in extreme meteorological environments.
subject: Research Article
  topic: section
subject: UAV control
  topic: keywords
subject: Extreme weather
  topic: keywords
subject: Disturbance observer
  topic: keywords
subject: Deep reinforcement learning
  topic: keywords
subject: Hybrid control
  topic: keywords
creator: HanJun Zhang
rights: Copyright (c) 2025 HanJun Zhang https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
starting page: 61
ending page: 65
doi: 10.61784/mjet3025

A HYBRID GENETIC-GRADIENT DESCENT ALGORITHM FOR OPTIMAL ELECTRIC VEHICLE CHARGING STATION PLACEMENT
type: item
description: The strategic deployment of charging infrastructure is important to accelerate the adoption of electric vehicle (EV) and reduce transportation emissions. However, optimal charging station placement presents a complex optimization challenge, constrained by multiple factors such as construction costs and user accessibility. Traditional optimization methods often struggle to find globally optimal solutions within this multi-dimensional constraint space. To address these challenges, we propose a novel hybrid optimization framework that integrates genetic algorithms (GA) with gradient descent (GD) methods for charging station location planning. Our approach uses GA to generate promising initial solutions, followed by gradient-based optimization for solution refinement. The methodology incorporates three variants of gradient descent, including adaptive, conditional, and proximal gradient. We evaluate our framework through comprehensive simulations across various scenarios, using a carefully designed virtual environment that models realistic user demand patterns and geographical constraints. The simulation results demonstrate the effectiveness and robustness of our proposed hybrid optimization framework for optimal charging station placement.
subject: Research Article
  topic: section
subject: Electric vehicle charging infrastructure
  topic: keywords
subject: Optimization
  topic: keywords
subject: Genetic algorithms
  topic: keywords
subject: Gradient descent
  topic: keywords
subject: Infrastructure planning
  topic: keywords
creator: XinRui Wang
rights: Copyright (c) 2025 XinRui Wang https://creativecommons.org/licenses/by/4.0
date: 2025-04-29
publication date: 2025-04-29
starting page: 66
ending page: 70
doi: 10.61784/mjet3026

EXPLORATION OF INDUSTRY-EDUCATION INTEGRATION MODEL FOR MEDICAL-ENGINEERING TALENT DEVELOPMENT IN THE CONTEXT OF DIGITAL LIVELIHOOD
type: item
description: This study explores innovative industry-education integration models for cultivating medical-engineering talents in higher education institutions within the context of digital livelihood. It analyzes both the current demands for talent driven by the upgrading of digital social services and the emerging trends in medical-engineering convergence, highlighting existing challenges and opportunities in talent development in this respect. Furthermore, drawing on successful experiences, this study suggests a new insight into industry-education collaboration model aimed at cultivating interdisciplinary talents in medical-engineering integration. Through theoretical teaching and practical training, the article integrates the three main entities of higher education institutions, hospitals, and enterprises to achieve integration in disciplines, curriculum, and platforms. Finally, the article details innovative strategies and specific implementation paths, including the construction of an interdisciplinary teaching system and a project-driven workshop practice system.
subject: Research Article
  topic: section
subject: Digital livelihood
  topic: keywords
subject: Medical-engineering talents
  topic: keywords
subject: Industry-education integration
  topic: keywords
subject: Workshops
  topic: keywords
creator: YuXin Xu
creator: TingLing Hu
rights: Copyright (c) 2025 YuXin Xu; TingLing Hu https://creativecommons.org/licenses/by/4.0
date: 2025-09-28
publication date: 2025-09-28
starting page: 1
ending page: 5
doi: 10.61784/mjet3030

ENERGY AND MAGNETIC FIELD DISTRIBUTION CHARACTERISTICS OF PM AND JONSWAP SPECTRA
type: item
description: To further investigate the formation mechanism, distribution characteristics, and distribution patterns of wave-induced magnetic fields, this study employs the Pierson-Moskowitz wave spectrum combined with the JONSWAP spectrum and Weaver electromagnetic theory. Using Monte Carlo random sampling, it simulates the dynamic characteristics of a two-dimensional sea surface under varying wind speeds. Maxwell's equations are applied to formulate expressions for the wave-induced magnetic field. The primary focus is simulating the three-dimensional spatial distribution and spectral characteristics of the induced magnetic field under different wind speed scenarios. Simulation results indicate: The PM spectrum is suitable for describing fully developed sea conditions characterized by uniform wave energy distribution and gentle waveforms. The JONSWAP spectrum is more applicable to developing wind-driven waves, exhibiting concentrated energy, steep waveforms, and higher induced magnetic field intensities. As wind speed increases, both spectra exhibit a trend of peak frequency migration toward lower frequencies. The induced magnetic field amplitude corresponding to the JONSWAP spectrum is significantly higher than that of the PM spectrum. These findings provide theoretical and simulation support for noise modeling and signal extraction in marine electromagnetic detection.
subject: Research Article
  topic: section
subject: Pierson-Moskowitz wave spectrum
  topic: keywords
subject: JONSWAP spectrum
  topic: keywords
subject: Wave-induced magnetic field
  topic: keywords
creator: XinTong Wang
creator: JianSheng Zhang
rights: Copyright (c) 2025 XinTong Wang; JianSheng Zhang https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 6
ending page: 16
doi: 10.61784/mjet3031

CONSTRUCTION TECHNOLOGY AND APPLICATION OF FRAME COLUMNS SUBJECTED TO ALTERNATING TENSION AND COMPRESSION FORCES USING THE REVERSE CONSTRUCTION METHOD
type: item
description: Based on a museum project along the Beijing-Hangzhou Grand Canal in China, this paper proposes a reverse construction technique for frame columns at beam-slab-column intersections where deformation differences exhibit irregular dynamic changes under loading. Based on finite element analysis of the stress characteristics of the frame columns and their connected beams and slabs, the reinforcement of the frame beams connected to the frame columns is strengthened to meet both the original design requirements and serve as temporary cantilever beams. The main structure outside the frame columns is constructed first, allowing the beams and slabs connected to the top and bottom of the frame columns to deform independently to a stable state. Finally, the frame columns are constructed. This technique effectively solves the quality problems caused by alternating tensile and compressive stresses in the frame columns due to deformation differences at the top and bottom; avoids the need for long-term temporary supports for beams and slabs, reducing construction difficulty and costs; eliminates the additional pressure transmitted to the bottom beams and slabs of the frame columns, improving structural safety redundancy; and accelerates the early interweaving of secondary structures and mechanical and electrical installations, shortening the overall construction period.
subject: Research Article
  topic: section
subject: Alternating tensile and compressive stress
  topic: keywords
subject: Frame column
  topic: keywords
subject: Reverse construction
  topic: keywords
creator: RuiDa Zong
creator: JiaLi Cao
creator: XiaoXia Zhao
creator: TianCi Zhu
creator: BaoAn Zhang
rights: Copyright (c) 2026 RuiDa Zong; JiaLi Cao; XiaoXia Zhao; TianCi Zhu; BaoAn Zhang https://creativecommons.org/licenses/by/4.0
date: 2026-01-23
publication date: 2026-01-23
starting page: 1
ending page: 7
doi: 10.61784/mjet3032

ANALYSIS, COMPARISON AND APPLICATION OF DRY AND WET DRILLING METHODS FOR ANTI-BUOYANCY ANCHOR BOLTS
type: item
description: With urbanization and the continuous expansion of building scale, the load-bearing capacity of underground structures is becoming increasingly prominent. Anti-buoyancy anchors, as a practical foundation method, have been widely used in various building projects, such as basements, underground garages, and bridge foundations. This study aims to systematically explain the processes of two technologies and compare and analyze them from the perspectives of construction methods, construction quality, and cost. It provides a scientific theoretical basis and technical guidance for engineering practice, helping engineers make more rational technical choices in actual construction, improving project quality, reducing project costs, and achieving safe, efficient, and sustainable construction.
subject: Research Article
  topic: section
subject: Anti-buoyancy anchor
  topic: keywords
subject: Construction technology
  topic: keywords
subject: Construction quality
  topic: keywords
creator: RenHong Sun
creator: ZongLin Yang
creator: ZhenHao Yao
creator: Bo Zhang
creator: ZhiYuan Zhang
creator: XiaoXia Zhao
creator: ZhiCheng Bai
rights: Copyright (c) 2026 RenHong Sun; ZongLin Yang; ZhenHao Yao; Bo Zhang; ZhiYuan Zhang; XiaoXia Zhao; ZhiCheng Bai https://creativecommons.org/licenses/by/4.0
date: 2026-01-26
publication date: 2026-01-26
starting page: 8
ending page: 12
doi: 10.61784/mjet3033

INTEGRATING INDIGENOUS KNOWLEDGE SYSTEMS INTO ADVANCED MATHEMATICAL FORMULA DEVELOPMENT: A FRAMEWORK FOR CURRICULUM INNOVATION IN SOUTHERN AFRICA
type: item
description: This doctoral research takes a step further to explore how IKS could be deployed to enhance the creation of even more complex formulas for advanced mathematics curricula, thereby providing a sound framework for curriculum enhancement in Southern Africa. It aims at bridging gaps of what Indigenous mathematics has yet to be allowed to contribute to modern mathematics education and higher education development. We use case studies, ethnomathematics, post-colonial education theory, and curriculum development models as discourses in this research to explore how IKS may potentially recast the rephoneticised formulation of mathematical equations in line with the socio-cultural realities of southern Africa. For this reason, the proposed framework revolves around three key elements. First, it supports conceptualizing and cataloguing Indigenous knowledge in mathematics and their rough appreciation as knowledge systems emanating from Indigenous peoples. Second, it focuses on integrating these ideas into current mathematical contexts—within the current storyline of mathematics, integration between traditional and innovative mathematical approaches. Last, it captures the re-contextualization of educational content in collaboration with Indigenous people, scholars, educators, and policymakers for relevance, equity, and practicality. This work also explores the issues involved in implementing IKS in formal education, where resources are scarce; Indigenous knowledge and skills are not valued; culture and timely entry are issues that are not given adequate attention, and there is a critical need to prepare teachers through other professional development programmes to enable them to provide for inclusive education. It outlines an approach to addressing these concerns, including creating culturally appropriate knowledge-sharing Radicals, co-designing curriculum with communities, and pursuing policies of equity. The research states that integrating IKS in formula derivation can redefin…
subject: Research Article
  topic: section
subject: IKSM
  topic: keywords
subject: Continuing the work of formulating and applying mathematical and other useful formulas
  topic: keywords
subject: curriculum development
  topic: keywords
subject: Southern Africa
  topic: keywords
creator: Stephen Kelvin Sata
rights: Copyright (c) 2025 Stephen Kelvin Sata https://creativecommons.org/licenses/by/4.0
date: 2025-01-22
publication date: 2025-01-22
starting page: 16
ending page: 28
doi: 10.61784/mjet3017

CONSTRUCTION TECHNOLOGY OF LARGE-SPAN CAVITY VENTILATION SHAFT WITH SMALL CLEARANCE ON ROOF
type: item
description: The top of the traditional cavity ventilation shaft adopts cast-in-place concrete structure. The formwork system can be removed only after the concrete reaches the strength. If the clearance of the cavity ventilation shaft is only 40cm-70cm, the construction workers cannot carry out construction in the narrow space. Failure to remove the formwork system not only affects the function of the ventilation shaft, but also causes waste of materials. For small-span cavities, the floor decking is constructed and cast in one go. However, in order to solve the fulcrum problem of large-span floor decking, the traditional method is to set concrete structural beams in the cavity as the support of the large-span floor decking. However, this method will affect the ventilation volume of the air shaft due to the horizontal components. In order to ensure that the cavity air shaft does not affect the use function, a construction method of a single floor deck connecting beam for a large-span cavity air shaft with small roof clearance is proposed. The research results show that the construction method of the small clearance and large span cavity wind shaft on the roof can effectively ensure the one-time casting effect of the roof and avoid the risk of roof leakage. This method ensures the use function of the wind shaft and the ventilation volume required by the design; the construction method of the small clearance and large span cavity wind shaft on the roof is convenient for construction, shortens the construction period, reduces costs, is green and sustainable, has reasonable force, is safe and reliable, and can effectively solve the construction difficulties encountered in the small clearance and large span cavity wind shaft; compared with the characteristics of traditional concrete structural beams as large span floor deck support, the cost saving rate of the optimized single floor deck connection beam construction of the small clearance and large span cavity wind shaft on the roof…
subject: Research Article
  topic: section
subject: Single floor deck connection beam
  topic: keywords
subject: Cavity wind shaft
  topic: keywords
subject: Floor deck
  topic: keywords
creator: YongYan Li
creator: Yang Yang
creator: XinXin Jin
creator: ZhiCheng Bai
creator: Jie Luo
creator: Kun Wu
rights: Copyright (c) 2024 YongYan Li; Yang Yang; XinXin Jin; ZhiCheng Bai; Jie Luo; Kun Wu https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 39
ending page: 42
doi: 10.61784/mjet3008

DIGITAL FORENSIC INVESTIGATION IN CYBERCRIME CASES: CASE STUDIES AND RECOMMENDATIONS
type: item
description: The exponential advancement of technology and widespread internet adoption have introduced numerous advantages while simultaneously giving rise to an alarming surge in cybercrime activities such as hacking, financial fraud, identity theft, and data breaches. Digital forensic investigation has emerged as a pivotal mechanism in addressing these crimes by identifying offenders, gathering actionable evidence, and aiding judicial processes. This paper delves into the utilization of digital forensic methods in resolving cybercrime incidents, drawing on a selection of illustrative case studies to showcase the field's strengths and limitations. The research begins with an overview of the foundational concepts and techniques in digital forensics, emphasizing its interdisciplinary approach that integrates computing, legal frameworks, and investigative practices. Detailed case studies illustrate practical applications, including tracing ransomware campaigns, uncovering insider breaches within corporate environments, and analyzing fraudulent cryptocurrency activities.
subject: Research Article
  topic: section
subject: Integrating insights
  topic: keywords
subject: Malware deconstruction
  topic: keywords
subject: Financial fraud
  topic: keywords
creator: Cut Rafifah Syaakirah
creator: Luthfiyah Syifa
creator: Iskandar Muda
rights: Copyright (c) 2025 Cut Rafifah Syaakirah; Luthfiyah Syifa; Iskandar Muda https://creativecommons.org/licenses/by/4.0
date: 2025-01-14
publication date: 2025-01-14
starting page: 9
ending page: 15
doi: 10.61784/mjet3018

A COMMAND SUPPORT MODEL BASED ON BLOCKCHAIN AND THE METAVERSE
type: item
description: Traditional command support systems face issues such as deficiencies in decision support, data silos, and risks of forgery and tampering, which significantly hinder the enhancement of combat effectiveness and support capabilities. The metaverse, through tools like visualization, immersive platforms, and scenario simulations, aids in decision-making. However, it is threatened by data security concerns and asset ownership issues. Fortunately, blockchain technology offers a higher level of trust and security for the metaverse, serving as a foundational technology to address its shortcomings while effectively facilitating data flow and integration, thus eliminating data silos. Based on a needs analysis, this paper proposes a command support system model integrating the metaverse and blockchain. It provides a detailed description of the model's construction and briefly introduces the challenges and solutions associated with this integration. This work offers valuable insights for aligning future intelligent equipment supply support needs with the demands of modern warfare.
subject: Research Article
  topic: section
subject: Metaverse
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Command support
  topic: keywords
subject: Military application
  topic: keywords
creator: ChaoYue Song
creator: YiLiang Han
creator: XuGuang Wu
rights: Copyright (c) 2024 ChaoYue Song; YiLiang Han; XuGuang Wu https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 43
ending page: 50
doi: 10.61784/mjet3009

ENHANCED UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION USING CONTRASTIVE LEARNING AND HISTOGRAM OF ORIENTED GRADIENTS
type: item
description: Image-to-Image Translation is a vital area of computer vision that focuses on transforming images from one visual domain to another while preserving their core content and structure. However, this field faces two major challenges: first, the data from the two domains are often unpaired, making it difficult to train generative adversarial networks effectively; second, existing methods tend to produce artifacts or hallucinations during image generation, leading to a decline in image quality. To address these issues, this paper proposes an enhanced unsupervised image-to-image translation method based on the Contrastive Unpaired Translation (CUT) model, incorporating Histogram of Oriented Gradients (HOG) features. This novel approach ensures the preservation of the semantic structure of images, even without semantic labels, by minimizing the loss between the HOG features of input and generated images. The method was tested on translating synthetic game environments from GTA5 dataset to realistic urban scenes in cityscapes dataset, demonstrating significant improvements in reducing hallucinations and enhancing image quality.
subject: Research Article
  topic: section
subject: Image-to-image translation
  topic: keywords
subject: Photorealism
  topic: keywords
subject: GANs
  topic: keywords
creator: WanChen Zhao
creator: WenHan Wang
creator: Cheng Zhang
creator: XiaoLei Qu
rights: Copyright (c) 2024 WanChen Zhao; WenHan Wang; Cheng Zhang; XiaoLei Qu https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 51
ending page: 59
doi: 10.61784/mjet3010

STUDY ON EQUIVALENT STATIC WIND LOAD ON LONG-SPAN ROOF BASED ON RESPONSE-RELATED SCALE
type: item
description: This paper proposes an equivalent static wind load calculation method based on response-related scale for the wind load calculation of large-span roof structures. The effectiveness of this method is verified by wind tunnel tests and finite element analysis of the roof of the east entrance of Hangzhou Convention and Exhibition Center. The research results show that the equivalent static wind load method based on response-related scale has high accuracy in reflecting the overall response characteristics of the structure, and its error in calculating the node displacement response is the smallest, only 4.6%, which is better than the traditional single-objective equivalent method and least squares method.
subject: Research Article
  topic: section
subject: Long-span roof
  topic: keywords
subject: Static wind load
  topic: keywords
subject: Response-related scale
  topic: keywords
subject: Equivalent load
  topic: keywords
subject: Hangzhou convention and exhibition center
  topic: keywords
creator: Yi Zhu
creator: ZhiCheng Bai
creator: XiaoXia Zhao
creator: YinGuang Wang
creator: Xu Zhang
rights: Copyright (c) 2024 Yi Zhu; ZhiCheng Bai; XiaoXia Zhao; YinGuang Wang; Xu Zhang https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 60
ending page: 65
doi: 10.61784/mjet3011

A REINFORCEMENT LEARNING FRAMEWORK FOR ACCURATE AND CONTEXT-AWARE LEGAL DOCUMENT SUMMARIZATION
type: item
description: This paper presents a novel reinforcement learning framework designed to enhance the accuracy and context-awareness of legal document summarization. In the contemporary legal environment, where professionals face an overwhelming volume of complex legal texts, the ability to generate concise and precise summaries is critical for informed decision-making. Traditional summarization techniques, including extractive and abstractive methods, often fall short in capturing the nuanced language and specific context inherent in legal documents. Our research addresses this gap by leveraging reinforcement learning to create a system that learns from feedback and adapts to the unique characteristics of legal texts. The framework incorporates a robust reward function that evaluates both the accuracy and contextual relevance of generated summaries, significantly improving summarization quality compared to existing methods. Empirical results demonstrate that our approach not only enhances the relevance of summaries but also maintains the integrity of legal terminology, providing legal practitioners with more meaningful insights. This study contributes to the ongoing evolution of legal technology, emphasizing the importance of context-aware summarization tools in improving access to legal information and enhancing decision-making processes.
subject: Research Article
  topic: section
subject: Reinforcement learning
  topic: keywords
subject: Legal document summarization
  topic: keywords
subject: Context-awareness
  topic: keywords
creator: JianKang Dong
creator: PingYu Lin
rights: Copyright (c) 2024 JianKang Dong; PingYu Lin https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 1
ending page: 9
doi: 10.61784/mjet3012

A KNOWLEDGE-DRIVEN FRAMEWORK FOR ENHANCING LEGAL DECISION SUPPORT WITH LARGE LANGUAGE MODELS
type: item
description: This paper proposes a knowledge-driven framework for enhancing legal decision support systems through the integration of Large Language Models. As legal professionals face increasing complexity and volume of information in their practice, traditional LDSS often fall short due to their reliance on rigid algorithms and insufficient adaptability to new legal precedents. The proposed framework emphasizes the systematic organization, representation, and acquisition of legal knowledge, enabling LLMs to provide more accurate and contextually relevant insights. By leveraging the advanced natural language processing capabilities of LLMs, the framework aims to streamline legal research, improve decision-making efficiency, and enhance the overall quality of legal practice. The findings suggest that this integration can significantly benefit legal professionals by allowing them to focus on substantive legal work while addressing ethical considerations associated with AI deployment in law. This paper calls for further collaboration between researchers and practitioners to refine these technologies and ensure their responsible use in the legal domain.
subject: Research Article
  topic: section
subject: Legal decision support systems
  topic: keywords
subject: Large language models
  topic: keywords
subject: Knowledge-driven framework
  topic: keywords
creator: ChenJian Wang
creator: YiMing Li
rights: Copyright (c) 2024 ChenJian Wang; YiMing Li https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 10
ending page: 16
doi: 10.61784/mjet3014

LEGAL RECOMMENDATION SYSTEMS: APPLICATIONS, TECHNOLOGIES, AND FUTURE DIRECTIONS IN THE DIGITAL AGE
type: item
description: The legal industry has seen a growing interest in the application of recommendation systems to streamline various legal workflows and enhance decision-making processes. Legal recommendation systems leverage data-driven algorithms to provide personalized advice, predict case outcomes, and assist legal professionals in tasks ranging from legal research to contract analysis. This review article explores the current state of legal recommendation systems, examining their key applications, underlying technologies, and the unique challenges that arise in the legal domain. The paper delves into the use of legal recommendation systems for tasks such as precedent retrieval, document summarization, litigation strategy formulation, and talent management. It analyzes the integration of advanced techniques like natural language processing, machine learning, and knowledge graphs to power these recommendation systems. Additionally, the review addresses the critical considerations surrounding data privacy, ethical implications, and the need for explainable and accountable systems within the legal industry. By synthesizing the existing research and industry trends, this review article serves as a comprehensive guide for legal professionals, technology providers, and researchers seeking to understand the transformative potential of recommendation systems in the legal field. It also highlights the emerging areas of focus, including the synergies between legal recommendation systems and emerging technologies like blockchain and artificial intelligence, and the evolving regulatory landscape governing the use of these systems.
subject: Research Article
  topic: section
subject: Legal recommendation systems
  topic: keywords
subject: Legal technology
  topic: keywords
subject: Legal research
  topic: keywords
subject: Litigation strategy
  topic: keywords
subject: Contract analysis
  topic: keywords
subject: Data-driven decision making
  topic: keywords
subject: Natural language processing
  topic: keywords
subject: Machine learning
  topic: keywords
creator: Sarah Chen
creator: Michael Rodriguez
rights: Copyright (c) 2024 Sarah Chen; Michael Rodriguez https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 17
ending page: 21
doi: 10.61784/mjet3015

DEVELOPMENT AND APPLICATION OF ELECTRICAL ENGINEERING IN ARTIFICIAL INTELLIGENCE AND BIG DATA ENVIRONMENT
type: item
description: With the rapid development of artificial intelligence and big data technology, the field of electrical engineering is undergoing profound changes. Based on the development trend and application status of electrical engineering in artificial intelligence and big data environment, this paper analyzes its influence on the development of traditional electrical engineering, its application in power system, power electronics, motor and electrical apparatus, automatic control, signal and information processing are also discussed. It will also focus on the challenges and opportunities faced by big data and artificial intelligence technology in electrical engineering applications, and look forward to the future development direction.
subject: Research Article
  topic: section
subject: Electrical engineering
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Big data
  topic: keywords
subject: Application
  topic: keywords
subject: Development trend
  topic: keywords
creator: DengKe Huang
rights: Copyright (c) 2024 DengKe Huang https://creativecommons.org/licenses/by/4.0
date: 2024-12-09
publication date: 2024-12-09
starting page: 22
ending page: 25
doi: 10.61784/mjet3016

SATELLITES SYSTEMS AND NIGERIAN COMMUNICATION SATELLITES (NIGCOMSAT): AN OVERVIEW
type: item
description: Satellites are specific or multipurpose man – made systems that operate in the space for various technologically related applications. This paper attempts a review of satellites systems and Nigerian Communication Satellites (NigComSat). Sub themes covered include: how satellites works, frequency bands, benefits of NigComSat 1R and that of satellites in general. The paper concluded by advocating a reformation of educational curriculum to make space technology a priority, supported with appropriate infrastructure; and effective marketing of the satellites communication facility. This is believed would place the country among the fore-runners in space technology.
subject: Research Article
  topic: section
subject: Satellites system
  topic: keywords
subject: Frequency bands
  topic: keywords
subject: NigComSat
  topic: keywords
creator: Alasi T. K.
creator: Adebisi A. C.
creator: Rasheed H. A.
rights: Copyright (c) 2024 Alasi T. K.; Adebisi A. C.; Rasheed H. A. https://creativecommons.org/licenses/by/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 26
ending page: 30
doi: 10.61784/mjet3013

PERFORMANCE COMPARISON OF CEEMDAN-LSTM AND BASIC LSTM MODELS IN PREDICTING REALIZED VOLATILITY
type: item
description: This paper presents a comparative analysis of the effectiveness of hybrid CEEMDAN-LSTM models and traditional LSTM models in predicting realized volatility in financial markets. By utilizing realized volatility data from 2004 to 2024, the study highlights significant market fluctuations during the 2008 financial crisis and the 2020 COVID-19 pandemic. The findings indicate that the CEEMDAN-LSTM model, which decomposes time series data into intrinsic mode functions (IMFs) before applying LSTM networks, outperforms the basic LSTM model in terms of predictive accuracy, particularly during periods of high volatility. This enhanced performance is evidenced by lower error metrics, such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). The research underscores the value of integrating advanced decomposition techniques with deep learning models to better capture the complex dynamics of financial markets.
subject: Research Article
  topic: section
subject: CEEMDAN-LSTM
  topic: keywords
subject: Intrinsic mode functions (IMFs)
  topic: keywords
subject: Mean Absolute Error (MAE)
  topic: keywords
subject: Mean Squared Error (MSE)
  topic: keywords
creator: ZheShuo Zhang
rights: Copyright (c) 2024 ZheShuo Zhang https://creativecommons.org/licenses/by/4.0
date: 2024-01-01
publication date: 2024-01-01
starting page: 34
ending page: 38
doi: 10.61784/mjet3007

EARLY BEARING FAULT DETECTION AND RECOGNITION METHOD BASED ON INSTANCE TRANSFER
type: item
description: Under the condition of data category imbalance, this paper proposes a TrAdaBoost-Least Squares Support Vector Machine (TrAdaBoost-LSSVM) algorithm based on instance transfer to solve the problem of low diagnostic accuracy of traditional machine learning methods. Firstly, use the K-means algorithm to screen the source domain data in order to eliminate those data with low similarity to the target domain, and then increase the inter-domain similarity; then, optimize the evaluation index of the base classifier to improve the model generalization ability. The simulation test results show that the method proposed in this paper exhibits the advantage of high fault recognition accuracy compared with the traditional machine learning method.
subject: Research Article
  topic: section
subject: Data category imbalance
  topic: keywords
subject: Instance transfer
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Least squares support vector machine
  topic: keywords
creator: Zhao Jiang
creator: Yu Wei
rights: Copyright (c) 2025 Zhao Jiang; Yu Wei https://creativecommons.org/licenses/by/4.0
date: 2025-01-08
publication date: 2025-01-08
starting page: 1
ending page: 8
doi: 10.61784/mjet3019