HighTech and Innovation Journal
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317 research outputs found
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Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization
Full text linkWithin the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for dense fingerprint databases. A hybrid two-stage clustering method, the CAP-DBSCAN algorithm, is proposed in this paper, designed to accelerate DBSCAN clustering while ensuring accuracy for fingerprint-based localisation systems. The CAP-DBSCAN algorithm employs the closest access point (CAP) algorithm to pre-cluster the database, while the DBSCAN algorithm performs clustering refinement. It dynamically adjusts the neighborhood radius (Eps) value for each pre-cluster using the k-distance plot method. The performance of the CAP-DBSCAN algorithm is determined across four publicly available received signal strength (RSS)-based fingerprint databases with Euclidean and Manhattan distances as fingerprint similarity metrics. This is benchmarked against the performances of the standard DBSCAN (s-DBSCAN) and k-means++-DBSCAN (k-DBSCAN) algorithms presented in previous research. Simulation results show that the CAP-DBSCAN algorithm consistently outperforms both the s-DBSCAN and k-DBSCAN algorithms, achieving higher silhouette scores, which indicates the generation of more compact and well-defined clusters. Furthermore, the CAP-DBSCAN algorithm demonstrates superior computational efficiency as a result of the CAP algorithm generating well-structured pre-clusters better than those generated by the k-means++ algorithm. This significantly reduces the computational burden of the cluster refinement process. Overall, using Manhattan distance as a fingerprint similarity metric results in the best clustering performance of the CAP-DBSCAN algorithm. These findings underscore the potential of the CAP-DBSCAN algorithm for practical applications in resource-constrained fingerprint-based localization systems. Doi: 10.28991/HIJ-2025-06-01-022 Full Text: PD
Noise Separation Techniques for Accurate Substation Anomaly Detection: An Intelligent Methodology
Full text linkTo better monitor and characterize sounds produced by substations, this study aims to separate sounds produced by the equipment from environmental ambient noise as a means of improving the relevancy (and ultimately reliability) of the power grid. To do so, we propose a deep learning-based noise monitoring system in an end-network-cloud architecture that enables remote data collection, analysis, and management. This is achieved by developing a deep learning-based noise monitoring system, enabling remote data collection, processing, and management. The proposed method consists of two basic components: a self-designed Panel Response Acquisition device that can collect sufficient acoustic information, and a refined Deep Belief Network (DBN) that is trained with a Dynamic version of the Dwarf Mongoose Optimizer (DDMO) to improve the accuracy of the noise separation process. The performance of the DBN/DDMO model is 13.1 dB for SI-SDRi and 15.7 dB for SDRi, which are large improvements for SI-SNRi and SDRi over AlexNet and CNN-VGG19. This approach minimizes SPL deviations, as shown by a thorough computation regarding several data sets; therefore, it guarantees precise noise quantification under disturbing sounds. By allowing for proactive identification of unusual noise levels, this research supports predictive maintenance methods that can avoid sudden failures and improve the overall reliability of substations
Multi-Objective Biomechanical Optimization of Breaststroke Swimming Using NSGA-II
Full text linkAdvancements in computational modeling and optimization algorithms have opened new possibilities for analyzing and improving sports biomechanics. This study presents a multi-objective optimization framework based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize breaststroke swimming techniques. The framework integrates a biomechanical model that combines hydrodynamic forces, joint kinematics, and energy expenditure to address three conflicting objectives: maximizing swimming velocity, improving energy efficiency, and minimizing joint load. Experimental validation conducted with professional swimmers demonstrated that the optimized stroke techniques achieved up to a 20% reduction in peak joint loads at the shoulder and knee, significantly reducing the risk of overuse injuries. Additionally, energy consumption per stroke cycle decreased by 15%-20%, while propulsion efficiency was notably enhanced. The framework generates Pareto-optimal solutions, offering a spectrum of trade-offs that can be tailored to individual performance goals and physical constraints. This approach provides a quantitative, data-driven alternative to traditional training methods, enabling personalized and informed decision-making for athletes and coaches. Beyond breaststroke, the methodology can be extended to other swimming techniques and athletic disciplines, addressing the interplay between performance, efficiency, and safety. This study bridges the gap between theoretical modeling and practical application, offering a scalable and robust solution for optimizing sports performance and reducing injury risks
Investigating the Correlation Between Bitcoin Trading Volume and Technical Indicators Using Data Mining Techniques
Full text linkThis study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoin’s daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = –0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volume–price–volatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management
Sociological Impact of Cyber Laws on Media: Virtue Community and Controversies
Full text linkThis study aims to delve into the sociological dimensions and repercussions of cyber law on cyber-based mass media and social media freedoms in Indonesia while shedding light on questionable aspects of the country's cyber laws. Employing qualitative research methods encompassing theoretical and investigative perspectives, the study combines normative juridical research with a legal, sociological approach and an observational survey approach. Data collection involved an examination of legal materials on information technology alongside interviews with cyber-policymaking bodies, stakeholders, cyber-media houses, journalists, bloggers, and social media influencers. The collected data were scrutinized through descriptive analysis to clarify the sociological and legal impacts of Indonesia's cyber law on media and social media. The findings reveal that implementing cyber law in Indonesia carries substantial sociological implications for both the media and society. It highlights questionable aspects of the existing cyber laws, as they pose challenges to upholding the rule of law and safeguarding social and media freedoms in the country. The insights derived from this study hold relevance for research endeavors focusing on the sociological aspects of cyber law in developing countries. This study contributes to a deeper understanding of the evolving digital landscape and emphasizes the need to address pertinent issues while balancing legal regulations and societal freedoms. Doi: 10.28991/HIJ-2024-05-01-04 Full Text: PD
Impact of Climate Change on the Performance of Household-Scale Photovoltaic Systems
Full text linkThe objective of this article was to investigate the impacts of climate change on photovoltaic systems among renewable energies by the end of the 21st century. One hypothesis posited that due to decreased cloud cover as a result of changing climate, the geographical region under examination would receive more solar irradiation”usable by photovoltaic panels”which would in turn increase the annual electrical energy production of these systems. Another hypothesis suggested that the average temperature increase, associated with changing climate conditions, would detrimentally affect the efficiency of electricity production in photovoltaic systems. The study was based on the simulation of a household-scale photovoltaic model. This simulation calculated the system's performance on an hourly basis depending on inputs and summed these to produce an annual value. Input values were derived from climate scenario databases. These variables included global horizontal irradiance, direct horizontal irradiance, temperature, and wind speed. The output was the aforementioned quantity of annual electrical energy production. An analysis occurred between the annual average global horizontal irradiance and the annual average air temperature in relation to the quantities of annual electrical energy production. Pearson and partial correlation examinations among the variables demonstrated that unfavorable scenarios resulted in reduced efficiency of photovoltaic electrical energy production, primarily due to rising temperatures. Among other contributions, this article can support research into the active cooling of photovoltaic systems and the examination of their viability to mitigate efficiency losses caused by current and future temperature increases. Doi: 10.28991/HIJ-2024-05-01-01 Full Text: PD
Integrating Intelligent Sensors for Safe UAV Distribution: Design and Evaluation of Ranging System
Full text linkIn our increasingly electrified society, electricity has become indispensable to both production and daily life. However, high-load electric energy transmission presents inherent safety risks. To ensure the secure transportation of electric energy, live work on distribution networks is essential. However, traditional live work techniques require a high level of dependence on worker experience, which frequently leads to inaccurate safety distances and degrades worker security. This study proposes the integration of Unmanned Aerial Vehicles (UAVs) with intelligent robot sensing technology to enhance safety and efficiency in live work. By accurately measuring safety distances, UAVs offer a promising solution to mitigate risks associated with high-voltage circuits. Comparative analysis between traditional and intelligent live work safety distance measurements reveals the two live works under the high voltage circuit were 92% and 96%, respectively, and the accuracy of the two live work safe distance measurements under the low voltage circuit was 84% and 99%, respectively. Results demonstrate that UAVs equipped with intelligent sensing technology achieve superior accuracy in safe ranging for live work, thereby ensuring stable energy transmission and safeguarding the lives of workers in distribution networks. Doi: 10.28991/HIJ-2024-05-03-04 Full Text: PD
Creating an Innovative Business Model for the Performance of Commercial Dental Clinics
Full text linkProviding dental care to the population is associated with the active introduction of new technologies, personnel management methods, and business processes. In this sense, dentistry is at the forefront of the development of medicine and other economic sectors. However, the active practical implementation of advanced technologies for the provision of dental services requires personnel to have increased motivation and highly qualified labor, to develop new protocols for patient management, and to use more advanced equipment and materials, while administrative and management personnel should introduce progressive methods of labor motivation and economic and mathematical models of material and moral stimulation. This research aims to create an innovative business model for the development of a commercial dental clinic (CDC) that provides paid dental services. Economic and mathematical modeling and nonlinear programming are aimed at maximizing dentists' wages, together with financial incentives for the work of administrative and managerial personnel and deductions for the development of a typical commercial dental clinic in Moscow based on the actual volume of dental services and the costs of their provision. With the volume of paid dental services growing by one and a half times, the innovative business model makes it possible to increase clinic income by a factor of 1.66 and dentists' salaries by a factor of 2.24, raise deductions for labor incentives for administrative and managerial personnel by a factor of 1.66, and increase total profit by a factor of 1.75. During the research, it was possible to ensure early repayment of a loan of 5 million rubles for clinic development in 21 months. Additional research is needed because of the possible variability of the dental market and lending conditions. Doi: 10.28991/HIJ-2024-05-01-05 Full Text: PD
5G Opportunities in the South Pacific: Leveraging Low-Band Spectrum for Socio-Economic Development
Full text linkThis paper explores the potential for deployment of 5G communication in the South Pacific, with a particular focus on leveraging the low-band spectrum for socio-economic development. The purpose of this study is to assess the feasibility of deploying 5G infrastructure in the South Pacific region, analyze the socio-economic benefits it may bring, and propose strategies to maximize these benefits. The research methodology includes a comprehensive review of existing literature on 5G deployment strategies, the socio-economic impacts of telecommunications infrastructure, and case studies of similar initiatives in other regions. The findings show that the deployment of 5G technology using low-band spectrum has the potential to significantly improve connectivity, healthcare, education, and economic opportunities in the South Pacific. Additionally, the paper proposes innovative approaches to address challenges such as infrastructure development in remote areas and affordability for marginalized communities. This study contributes to existing literature by providing tailored recommendations for leveraging 5G technology to address socio-economic inequalities in the South Pacific, thereby contributing to the development of telecommunications infrastructure in the region. Provides a new perspective on the possibilities of structure. Doi: 10.28991/HIJ-2024-05-02-020 Full Text: PD
An Effective Model of Viral Marketing for e-Commerce Enterprises: An Empirical Study
Full text linkDespite the widespread significance of digital marketing in disseminating information about products and services across a vast customer base via diverse networks, a noteworthy proportion of businesses still struggle to comprehend the crucial factors underpinning the success of viral campaigns. This study aims not only to bridge this knowledge gap but also to introduce an innovative framework that underscores various factors that amplify the potency of social networks and emphasizes an often-overlooked element in customer engagement: the psychological state of customers. Empirical validation of the framework was conducted using a sample of 135 respondents, which was analyzed using the structured equation modeling technique. The study's findings show that the strength of social connections (strong ties) and the psychological disposition of customers significantly shape the generation and viral dissemination of marketing content across diverse networks. The importance of this research lies in its potential application by commercial companies for conducting promotional and marketing campaigns. By leveraging the proposed model, businesses can effectively promote their products and services, thus achieving their strategic objectives and gaining a competitive advantage in an environment characterized by intense competition and constant change. Doi: 10.28991/HIJ-2024-05-01-011 Full Text: PD