Multidisciplinary Journal of Engineering and Technology
Multidisciplinary Journal of Engineering and Technology (MJET) is an international peer-reviewed, open access journal.
THE HIGH AND LOW TEMPERATURE TEST SCHEME OF DC-DC CONVERTERS
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.
Copyright (c) 2026 ZhiWei Ma https://creativecommons.org/licenses/by/4.0
EXPERIMENTAL TEST OF COMBUSTION AND EXPLOSION PRESSURE OF METHANE-AIR PREMIXED GAS
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.
Copyright (c) 2026 XianRen Zeng; Ke Yang; QingHeng Zeng; LinMei Li; ZhiYue Chen https://creativecommons.org/licenses/by/4.0
CONSTRUCTION TECHNIQUES AND APPLICATIONS FOR CRACK PREVENTION IN CANTILEVERED MULTI-STORY LARGE-SPAN STEEL-REINFORCED CONCRETE WALLS
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.
Copyright (c) 2026 GuangLiang Qiu; Jie Luo; XiaoXia Zhao; BoBo Li; BaoAn Zhang https://creativecommons.org/licenses/by/4.0
MAPPING CULTURAL ART GENES: A VISUAL GENEALOGY FRAMEWORK FOR CULTURAL REPRESENTATION AND TRANSLATION
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.
Copyright (c) 2026 WeiXin Zhu; Lan Liu https://creativecommons.org/licenses/by/4.0
INTEGRATING INDIGENOUS KNOWLEDGE SYSTEMS INTO ADVANCED MATHEMATICAL FORMULA DEVELOPMENT: A FRAMEWORK FOR CURRICULUM INNOVATION IN SOUTHERN AFRICA
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 redefine education through increased students’ participation, enhanced diversity learning, and ultimately advocating Indigenous cultures. Moreover, it situates this integration as an enabler of future-oriented Implications, a new way of thinking mathematically for solving global problems using the architecture of both old and new knowledge systems. In conclusion, this work aims to enable the sharing of knowledge between indigenous and formal education systems towards prescribing radical changes to curriculum in Southern Africa. It imagines a future in which mathematical education not only represents but also appreciates various forms of knowledge in the region aenhancing the learning climates suitable for the global community. This work forms a platform on which postgraduate scholars, educators and policymakers can collectively reimagine and reposition Indigenous knowledge within advanced mathematical learning and academic pursuit.
Continuing the work of formulating and applying mathematical and other useful formulas
Copyright (c) 2025 Stephen Kelvin Sata https://creativecommons.org/licenses/by/4.0
RESOURCE SCHEDULING STRATEGY FOR INTERNET OF THINGS EDGE COMPUTING BASED ON ARTIFICIAL POTENTIAL FIELD ALGORITHM
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.
Copyright (c) 2025 ZongYang Du https://creativecommons.org/licenses/by/4.0
THERMAL EFFECTS ON THE LD END-FACE PUMPED ND:YAG LASER MEDIUM
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.
Copyright (c) 2025 XingYu Liu; JianSheng Zhang; Wen Du https://creativecommons.org/licenses/by/4.0
DESIGN OF TRAM ALGORITHM BASED ON GRAPH TRANSFORMATION
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.
Copyright (c) 2025 Ting Sun; ZhiHua Hu https://creativecommons.org/licenses/by/4.0
AI FOR CREDIT RISK MODELING: A DEEP LEARNING APPROACH
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.
Copyright (c) 2025 Kaoru Fujisawa; Renji Takeda; Aiko Mori https://creativecommons.org/licenses/by/4.0
A BIOMIMETIC BAT FLAPPING VEHICLE DESIGN FOR ENVIRONMENTAL MONITORING APPLICATIONS
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.
Copyright (c) 2025 ZhiWen Hou; Yang Xiang https://creativecommons.org/licenses/by/4.0
UAV ATTITUDE AND ALTITUDE STABILITY CONTROL ALGORITHM UNDER EXTREME WEATHER CONDITIONS
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.
Copyright (c) 2025 HanJun Zhang https://creativecommons.org/licenses/by/4.0
A HYBRID GENETIC-GRADIENT DESCENT ALGORITHM FOR OPTIMAL ELECTRIC VEHICLE CHARGING STATION PLACEMENT
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.
Electric vehicle charging infrastructure
Copyright (c) 2025 XinRui Wang https://creativecommons.org/licenses/by/4.0
EXPLORATION OF INDUSTRY-EDUCATION INTEGRATION MODEL FOR MEDICAL-ENGINEERING TALENT DEVELOPMENT IN THE CONTEXT OF DIGITAL LIVELIHOOD
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.
Copyright (c) 2025 YuXin Xu; TingLing Hu https://creativecommons.org/licenses/by/4.0
ENERGY AND MAGNETIC FIELD DISTRIBUTION CHARACTERISTICS OF PM AND JONSWAP SPECTRA
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.
Copyright (c) 2025 XinTong Wang; JianSheng Zhang https://creativecommons.org/licenses/by/4.0
CONSTRUCTION TECHNOLOGY AND APPLICATION OF FRAME COLUMNS SUBJECTED TO ALTERNATING TENSION AND COMPRESSION FORCES USING THE REVERSE CONSTRUCTION METHOD
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.
Alternating tensile and compressive stress
Copyright (c) 2026 RuiDa Zong; JiaLi Cao; XiaoXia Zhao; TianCi Zhu; BaoAn Zhang https://creativecommons.org/licenses/by/4.0
ANALYSIS, COMPARISON AND APPLICATION OF DRY AND WET DRILLING METHODS FOR ANTI-BUOYANCY ANCHOR BOLTS
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.
Copyright (c) 2026 RenHong Sun; ZongLin Yang; ZhenHao Yao; Bo Zhang; ZhiYuan Zhang; XiaoXia Zhao; ZhiCheng Bai https://creativecommons.org/licenses/by/4.0
EARLY BEARING FAULT DETECTION AND RECOGNITION METHOD BASED ON INSTANCE TRANSFER
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.
Copyright (c) 2025 Zhao Jiang; Yu Wei https://creativecommons.org/licenses/by/4.0
DIGITAL FORENSIC INVESTIGATION IN CYBERCRIME CASES: CASE STUDIES AND RECOMMENDATIONS
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.
Copyright (c) 2025 Cut Rafifah Syaakirah; Luthfiyah Syifa; Iskandar Muda https://creativecommons.org/licenses/by/4.0
PERFORMANCE COMPARISON OF CEEMDAN-LSTM AND BASIC LSTM MODELS IN PREDICTING REALIZED VOLATILITY
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.
Copyright (c) 2024 ZheShuo Zhang https://creativecommons.org/licenses/by/4.0
CONSTRUCTION TECHNOLOGY OF LARGE-SPAN CAVITY VENTILATION SHAFT WITH SMALL CLEARANCE ON ROOF
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 is about 88%, which is worthy of further promotion and application.
Copyright (c) 2024 YongYan Li; Yang Yang; XinXin Jin; ZhiCheng Bai; Jie Luo; Kun Wu https://creativecommons.org/licenses/by/4.0
A COMMAND SUPPORT MODEL BASED ON BLOCKCHAIN AND THE METAVERSE
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.
Copyright (c) 2024 ChaoYue Song; YiLiang Han; XuGuang Wu https://creativecommons.org/licenses/by/4.0
ENHANCED UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION USING CONTRASTIVE LEARNING AND HISTOGRAM OF ORIENTED GRADIENTS
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.
Copyright (c) 2024 WanChen Zhao; WenHan Wang; Cheng Zhang; XiaoLei Qu https://creativecommons.org/licenses/by/4.0
STUDY ON EQUIVALENT STATIC WIND LOAD ON LONG-SPAN ROOF BASED ON RESPONSE-RELATED SCALE
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.
Hangzhou convention and exhibition center
Copyright (c) 2024 Yi Zhu; ZhiCheng Bai; XiaoXia Zhao; YinGuang Wang; Xu Zhang https://creativecommons.org/licenses/by/4.0
A REINFORCEMENT LEARNING FRAMEWORK FOR ACCURATE AND CONTEXT-AWARE LEGAL DOCUMENT SUMMARIZATION
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.
Copyright (c) 2024 JianKang Dong; PingYu Lin https://creativecommons.org/licenses/by/4.0
A KNOWLEDGE-DRIVEN FRAMEWORK FOR ENHANCING LEGAL DECISION SUPPORT WITH LARGE LANGUAGE MODELS
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.
Copyright (c) 2024 ChenJian Wang; YiMing Li https://creativecommons.org/licenses/by/4.0
LEGAL RECOMMENDATION SYSTEMS: APPLICATIONS, TECHNOLOGIES, AND FUTURE DIRECTIONS IN THE DIGITAL AGE
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.
Copyright (c) 2024 Sarah Chen; Michael Rodriguez https://creativecommons.org/licenses/by/4.0
DEVELOPMENT AND APPLICATION OF ELECTRICAL ENGINEERING IN ARTIFICIAL INTELLIGENCE AND BIG DATA ENVIRONMENT
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.
Copyright (c) 2024 DengKe Huang https://creativecommons.org/licenses/by/4.0
SATELLITES SYSTEMS AND NIGERIAN COMMUNICATION SATELLITES (NIGCOMSAT): AN OVERVIEW
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.
Copyright (c) 2024 Alasi T. K.; Adebisi A. C.; Rasheed H. A. https://creativecommons.org/licenses/by/4.0
IDENTIFICATION AND TRACKING OF AERIAL UAVS BASED ON DEEP LEARNING VISUAL ALGORITHMS
When conducting long-range wireless charging and monitoring of Unmanned Aerial Vehicles (UAVs) in the air, remote identification and tracking of the drones in the air are required. To address this issue, a deep learning-based algorithm for aerial UAVs identification and tracking is proposed. The YOLO algorithm is utilized for UAVs identification in the air, and the Deep Sort algorithm is used for tracking the identified UAVs. A model and training dataset are constructed, and the deep learning model is trained. The trained model is then invoked to verify the identification and tracking effectiveness of the UAVs in the air.
Copyright (c) 2024 JianJun Song https://creativecommons.org/licenses/by/4.0
SISM: A SELF-INTERACTIVE APPROACH TO WEB NEWS SUMMARIZATION USING DEEP LEARNING
The exponential growth of online information has intensified the need for efficient web news summarization techniques. This paper introduces the Self-Interactive Summarization Model (SISM), a novel approach that combines advanced deep learning methods with an innovative refined tuning process. SISM employs a two-stage strategy: extensive pre-training on a diverse dataset, followed by a specialized fine-tuning phase. We present a new, carefully curated dataset that reflects the varied nature of web news articles, enabling comprehensive model evaluation. Our experiments demonstrate SISM's superiority over existing state-of-the-art models, with significant improvements in ROUGE scores across multiple test sets. The study highlights the critical role of our refined tuning process in enhancing summarization quality and adaptability to diverse news content. SISM's performance underscores its potential to advance the field of automated web news summarization, offering more accurate and contextually relevant summaries.
Copyright (c) 2024 Aleksy Nowaks https://creativecommons.org/licenses/by/4.0