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Journal articles on the topic 'Self-demand transformer'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

P, Maithili, and Kanakaraj J. "Transformer Less Self-Commutated PV Inverter." Regular issue 10, no. 8 (2021): 1–4. http://dx.doi.org/10.35940/ijitee.g9037.0610821.

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The power demand is increased day by day and generation of electrical energy from non-renewable sources are not able to meet the demand. An alternate energy sources are the only solution to meet the power demand. The power generation from solar energy with photovoltaic effect is plays a major role. This Solar PV system has low efficiency. The power semiconductor devices and converter circuit along with inductive / magnetic circuit. The Inverter circuit have an influence on photovoltaic power generation to improve the level of output voltage along with efficiency. In this paper a new transforme

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Maithili, P., and J. Kanakaraj. "Transformer Less Self-Commutated PV Inverter." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 8 (2021): 1–4. https://doi.org/10.35940/ijitee.G9037.0610821.

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The power demand is increased day by day and generation of electrical energy from non-renewable sources are not able to meet the demand. An alternate energy sources are the only solution to meet the power demand. The power generation from solar energy with photovoltaic effect is plays a major role. This Solar PV system has low efficiency. The power semiconductor devices and converter circuit along with inductive / magnetic circuit. The Inverter circuit have an influence on photovoltaic power generation to improve the level of output voltage along with efficiency. In this paper a new transforme

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Wang, Zhixing, Gaofan Zhou, Jinzhen Yao, Jianlin Zhang, Qiliang Bao, and Qintao Hu. "Self-Prompting Tracking: A Fast and Efficient Tracking Pipeline for UAV Videos." Remote Sensing 16, no. 5 (2024): 748. http://dx.doi.org/10.3390/rs16050748.

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In the realm of visual tracking, remote sensing videos captured by Unmanned Aerial Vehicles (UAVs) have seen significant advancements with wide applications. However, there remain challenges to conventional Transformer-based trackers in balancing tracking accuracy and inference speed. This problem is further exacerbated when Transformers are extensively implemented at larger model scales. To address this challenge, we present a fast and efficient UAV tracking framework, denoted as SiamPT, aiming to reduce the number of Transformer layers without losing the discriminative ability of the model.

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Ghimire, Sujan, Thong Nguyen-Huy, Mohanad S. AL-Musaylh, Ravinesh C. Deo, David Casillas-Pérez, and Sancho Salcedo-Sanz. "Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables." Energy and AI 14 (October 2023): 100302. http://dx.doi.org/10.1016/j.egyai.2023.100302.

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Gomathy, V., and S. Sumathi. "A Hybrid Optimization Technique for Fault Classification in Power Transformer Using Dissolved Gas Analysis." Applied Mechanics and Materials 573 (June 2014): 708–15. http://dx.doi.org/10.4028/www.scientific.net/amm.573.708.

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To allow utilities to fulfill self-imposed and regulative performance targets the demand for new optimized tools and techniques to Estimate the performance of modern Transformers has increased. The modern power transformers has subjected to different types of faults, which affect the continuity of power supply which in turn causes serious economic losses. To avoid the interruption of power supply, various fault diagnosis approaches are adopted to detect faults in the power transformer and has to eliminate the impacts of the faults at the initial stage. Among the fault diagnosis methods, the hy

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Zhang, Yong, Wee Hoe Tan, and Zijian Zeng. "Tourism Demand Forecasting Based on a Hybrid Temporal Neural Network Model for Sustainable Tourism." Sustainability 17, no. 5 (2025): 2210. https://doi.org/10.3390/su17052210.

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This paper introduces a novel hybrid forecasting model for tourism demand that combines Bidirectional Long Short-Term Memory (BiLSTM) and Transformer networks, addressing the challenge of capturing both short-term fluctuations and long-term trends in complex tourism data. Unlike traditional models, such as ARIMA, which often struggle with nonlinear patterns, our hybrid approach leverages the sequential learning capabilities of BiLSTM and the self-attention mechanism of the Transformer to effectively model intricate temporal dependencies. Our experiments on Thailand’s domestic tourism data show

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Guo, Jun, Tiancheng Li, and Baigang Du. "Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection." Applied Sciences 13, no. 16 (2023): 9109. http://dx.doi.org/10.3390/app13169109.

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Current methodologies for insulator defect detection are hindered by limitations in real-world applicability, spatial constraints, high computational demand, and segmentation challenges. Addressing these shortcomings, this paper presents a robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface. Firstly, the original backbone network is replaced by the transformer cross-stage partial (Transformer

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Huang, Jiahui, and Chenglong Zhang. "Daily Tourism Demand Forecasting with the iTransformer Model." Sustainability 16, no. 23 (2024): 10678. https://doi.org/10.3390/su162310678.

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Accurate forecasting of tourist volume is crucial for the sustainable development of the tourism industry. Deep-learning methods based on multivariate data can enhance the accuracy of tourism demand forecasting, enabling tourism management departments and enterprises to make evidence-based decisions. This study adopts an inverted transformer approach with a self-attention mechanism, which can improve the extraction of correlation features from the time series of multiple variables. Taking Zhejiang Province, a major tourist destination in China, and Hangzhou, a famous tourist city in China, as

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Yang, Jielin, Wanyi Li, and Mingqi Zheng. "Gesture Transformer: A Hybrid CNN-Transformer Model for Hand Gesture Recognition in Smart Educational Environments." Modern Intelligent Times 3 (July 7, 2025): 1. https://doi.org/10.53964/mit.2025001.

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Objective: With the increasing adoption of digital technologies in modern classrooms, there is a growing demand for intuitive and contactless modes of human-computer interaction. Traditional input methods such as keyboards and mice are often unsuitable in dynamic or inclusive educational environments. This study aims to address this need by developing a high-precision, real-time hand gesture recognition system, designed specifically for smart classroom applications. The goal is to empower educators and students to interact with digital content seamlessly through natural hand movements, thereby

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Cui, Jing, Yang Li, Jiaolong Liu, Jie Li, Zheng Yang, and Chunlin Yin. "Efficient Self-attention with Relative Position Encoding for Electric Power Load Forecasting." Journal of Physics: Conference Series 2205, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2205/1/012009.

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Abstract To effectively mine historical data information and improve the accuracy of short-term load prediction, this paper aims at the characteristics of time series and nonlinear power load. Deep learning for load forecasting has received a lot of attention in recent years, and it has become popular in the analysis of electricity load forecasting. Long short-term memory (LSTM) and gated recurrent unit (GRU) are specifically designed for time-series data. However, due to the gradient disappearing and exploding problem, recurrent neural networks (RNNs) cannot capture long-term dependence. The

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Parsola, Jyoti. "Exploring the Power of Transformer Models in Hospitality Domain." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 324–30. http://dx.doi.org/10.17762/msea.v70i1.2314.

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    ABSTRACT Despite decades of medical advancements and a rising interest in precision healthcare, the great majority of diagnoses are made after patients start to exhibit observable symptoms of sickness. However, early disease indication and detection can give patients and caregivers the opportunity for early intervention, better disease management, and effective use of healthcare resources. Deep learning and other recent advancements in machine learning provide a fantastic chance to fill this unmet demand. Transformer designs are very expressive because they en

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Hong, Yong, Deren Li, Shupei Luo, Xin Chen, Yi Yang, and Mi Wang. "An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention." Remote Sensing 14, no. 24 (2022): 6354. http://dx.doi.org/10.3390/rs14246354.

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Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction backbone network was combined with a self-built raster semantic map which was stored in the encoder for correlation and generated target position encoding and multi-dimensional feature vectors. The decoder incorpor

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Gharawi, Abdulrahman, Mohammad D. Alahmadi, and Lakshmish Ramaswamy. "Self-Supervised Skin Lesion Segmentation: An Annotation-Free Approach." Mathematics 11, no. 18 (2023): 3805. http://dx.doi.org/10.3390/math11183805.

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Skin cancer poses a significant health risk, affecting multiple layers of the skin, including the dermis, epidermis, and hypodermis. Melanoma, a severe type of skin cancer, originates from the abnormal proliferation of melanocytes in the epidermis. Current methods for skin lesion segmentation heavily rely on large annotated datasets, which are costly, time-consuming, and demand specialized expertise from dermatologists. To address these limitations and improve logistics in dermatology practices, we present a self-supervised strategy for accurate skin lesion segmentation in dermatologist images

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Kim, Hyungjoon, Yunju Kim, and Wonho Song. "SkinSavvy2: Augmented Skin Lesion Diagnosis and Personalized Medical Consultation System." Electronics 14, no. 5 (2025): 969. https://doi.org/10.3390/electronics14050969.

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The shortage of medical personnel and the busy lives of modern people have increased the desire for the self-diagnosis of diseases, and the latest large-scale language models and image recognition technologies have the potential to meet this demand. In particular, skin-related diseases are one of the areas where symptoms are visually distinguishable, making self-diagnosis and care possible. In this paper, we propose a system that classifies diseases through images of skin diseases and combines them with individual conditions such as age, skin type, and gender for self-diagnosis. First, we desi

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Yan, Xiang, Bing Han, Zhigang Su, and Jingtang Hao. "SignalFormer: Hybrid Transformer for Automatic Drone Identification Based on Drone RF Signals." Sensors 23, no. 22 (2023): 9098. http://dx.doi.org/10.3390/s23229098.

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With the growing integration of drones into various civilian applications, the demand for effective automatic drone identification (ADI) technology has become essential to monitor malicious drone flights and mitigate potential threats. While numerous convolutional neural network (CNN)-based methods have been proposed for ADI tasks, the inherent local connectivity of the convolution operator in CNN models severely constrains RF signal identification performance. In this paper, we propose an innovative hybrid transformer model featuring a CNN-based tokenization method that is capable of generati

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Yang, Jin, Xinyun Jiang, Gang Liang, Siyu Li, and Zicheng Ma. "Malicious Traffic Identification with Self-Supervised Contrastive Learning." Sensors 23, no. 16 (2023): 7215. http://dx.doi.org/10.3390/s23167215.

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As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The proposed method is able to overcome the shortcomings of traditional methods that rely on labeled samples and is able to learn data feature representations carrying semantic information from unlabeled data, thus improving the model accuracy. In this paper, a new malicious traffic feature extraction mod

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Chen, Haozhe, and Xiaojuan Zhang. "CGA-MGAN: Metric GAN Based on Convolution-Augmented Gated Attention for Speech Enhancement." Entropy 25, no. 4 (2023): 628. http://dx.doi.org/10.3390/e25040628.

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In recent years, neural networks based on attention mechanisms have seen increasingly use in speech recognition, separation, and enhancement, as well as other fields. In particular, the convolution-augmented transformer has performed well, as it can combine the advantages of convolution and self-attention. Recently, the gated attention unit (GAU) was proposed. Compared with traditional multi-head self-attention, approaches with GAU are effective and computationally efficient. In this CGA-MGAN: MetricGAN based on Convolution-augmented Gated Attention for Speech Enhancement, we propose a network

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Dubey, Parul, Pushkar Dubey, and Pitshou N. Bokoro. "Transformer-Driven Fault Detection in Self-Healing Networks: A Novel Attention-Based Framework for Adaptive Network Recovery." Machine Learning and Knowledge Extraction 7, no. 3 (2025): 67. https://doi.org/10.3390/make7030067.

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Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenge

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Tao, Xuewei, Shike Guo, Ziheng Zhang, and Xuchu Wu. "Image Enhancement Model for Open-Pit Mine Monitoring Based on Parallel Multi-Scale Feature Fusion." Information Technology and Control 54, no. 1 (2025): 5–15. https://doi.org/10.5755/j01.itc.54.1.38427.

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The workspace in open-pit mining systems often suffers from insufficient or uneven illumination due to spatial constraints and obstructions caused by large equipment or geotechnical structures, leading to degraded surveillance imagery and consequently impacting safety monitoring efforts. This study designed an open-pit mine surveillance image enhancement model based on a parallel multi-scale feature fusion Transformer to address the degradation of surveillance video images and leverage the superior expressive power of Transformer networks in visual image processing compared to other networks.

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Lin, Zhijie, Zilong Zhu, Lingling Guo, Jingjing Chen, and Jiyi Wu. "Disease Detection Algorithm for Tea Health Protection Based on Improved Real-Time Detection Transformer." Applied Sciences 15, no. 4 (2025): 2063. https://doi.org/10.3390/app15042063.

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Traditional disease detection methods typically depend on visual assessments conducted by human experts, which are time-consuming and subjective. Thus, there is an urgent demand for automated and efficient approaches to accurately detect and classify tea diseases. This study presents an enhanced Real-Time Detection Transformer (RT-DETR), tailored for the accurate and efficient identification of tea diseases in natural environments. The proposed method integrates three novel components: Faster-LTNet, CG Attention Module, and RMT Spatial Prior Block, to significantly improve computational effici

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Wang, Yuxue, Wei Liu, Shaolin Jiao, Zeya Fang, Yiquan Li, and Minghao Wen. "Substation-area Backup Protection Scheme for the Failure of 10kV Line Protection Device." Journal of Physics: Conference Series 2087, no. 1 (2021): 012009. http://dx.doi.org/10.1088/1742-6596/2087/1/012009.

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Abstract The research on wide-area backup protection for high-voltage transmission lines has made certain progress in recent years, but there are few reports published on substation-area backup protection for low-voltage 10kV transmission lines. Based on the characteristics of shared information in intelligent substation, a substation-area backup protection scheme to deal with the failure of 10kV line protection device is proposed in this paper. This scheme uses the device self-test information and heartbeat mechanism to monitor the operation status of 10kV line protection devices; to realize

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Hooda, Prince, and Mukesh Kumar Saini. "Reactive power planning with the help of multi-objective genetic algorithm and flexible AC transmission systems devices." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 1908–18. http://dx.doi.org/10.11591/beei.v12i4.5229.

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In this paper power quality of 3-bus solar-based hybrid system has been presented (where one or more than one distribution generator unit is connected to the grid). The injection of solar power into grid-connected systems creates power quality problems such as current consistency, electrical fluctuations, and inefficient power demand. A power quality control strategy based on a real-time self-regulation method for autonomous microgrid operation has been presented. In this paper solar farm design and satisfactory performance tests such as PV-static synchronous compensator (STATCOM) to improve t

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Hooda, Prince, and Mukesh Kumar Saini. "Reactive power planning with the help of multi-objective genetic algorithm and flexible AC transmission systems devices." Bulletin of Electrical Engineering and Informatics 12, no. 4 (2023): 1908–18. http://dx.doi.org/10.11591/eei.v12i4.5229.

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In this paper power quality of 3-bus solar-based hybrid system has been presented (where one or more than one distribution generator unit is connected to the grid). The injection of solar power into grid-connected systems creates power quality problems such as current consistency, electrical fluctuations, and inefficient power demand. A power quality control strategy based on a real-time self-regulation method for autonomous microgrid operation has been presented. In this paper solar farm design and satisfactory performance tests such as PV-static synchronous compensator (STATCOM) to improve t

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Sahil Yadav. "Adaptive AI-Driven Network Orchestration for Self-Evolving Enterprise Data Platforms." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1580–89. https://doi.org/10.30574/wjaets.2025.15.3.1087.

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This article presents a comprehensive theoretical framework for adaptive AI-driven network orchestration in enterprise data platforms, addressing the growing complexity and dynamic nature of modern data environments. The article introduces a self-evolving architectural construct that leverages advanced machine learning methodologies, specifically multi-agent reinforcement learning with proximal policy optimization, transformer-based anomaly detection, and temporal graph attention networks, to continuously monitor, predict, and optimize system resources without human intervention. The theoretic

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Bai, Weihua, Jiaxian Zhu, Jialing Zhao, Wenwei Cai, and Keqin Li. "An Unsupervised Multi-Dimensional Representation Learning Model for Short-Term Electrical Load Forecasting." Symmetry 14, no. 10 (2022): 1999. http://dx.doi.org/10.3390/sym14101999.

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The intelligent electrical power system is a comprehensive symmetrical system that controls the power supply and power consumption. As a basis for intelligent power supply control, load demand forecasting in power system operation management has attracted considerable research attention in energy management. In this study, we proposed a novel unsupervised multi-dimensional feature learning forecasting model, named MultiDBN-T, based on a deep belief network and transformer encoder to accurately forecast short-term power load demand and implement power generation planning and scheduling. In the

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Rupal Chaudhari. "Performance Analysis of Transformer Based Models for Automatic Short Answer Grading." Journal of Information Systems Engineering and Management 10, no. 51s (2025): 150–64. https://doi.org/10.52783/jisem.v10i51s.10376.

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Automatic Short Answer Grading (ASAG) has gained increasing importance in educational technology, where accurate and scalable assessment solutions are needed. Recent advances in Natural Language Processing (NLP) have introduced powerful Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), Text-to-Text Transfer Transformer (T5), and Generative Pre-trained Transformer 3 (GPT-3), which have demonstrated state-of-the-art performance across various text-based tasks. This paper presents a comparative study of these three models in the context of ASAG, eva

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Rupal Chaudhari. "Performance Analysis of Transformer Based Models for Automatic Short Answer Grading." Journal of Information Systems Engineering and Management 10, no. 51s (2025): 321–34. https://doi.org/10.52783/jisem.v10i51s.10392.

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Automatic Short Answer Grading (ASAG) has gained increasing importance in educational technology, where accurate and scalable assessment solutions are needed. Recent advances in Natural Language Processing (NLP) have introduced powerful Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), Text-to-Text Transfer Transformer (T5), and Generative Pre-trained Transformer 3 (GPT-3), which have demonstrated state-of-the-art performance across various text-based tasks. This paper presents a comparative study of these three models in the context of ASAG, eva

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Xing, Xin, Gongbo Liang, Chris Wang, Nathan Jacobs, and Ai-Ling Lin. "Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder." Bioengineering 10, no. 8 (2023): 901. http://dx.doi.org/10.3390/bioengineering10080901.

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The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imaging. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images. Our approach involved performing imaging reconstruction using a Vision Transf

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Sarker, Md Tanjil, Marran Al Qwaid, Siow Jat Shern, and Gobbi Ramasamy. "AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments." World Electric Vehicle Journal 16, no. 7 (2025): 385. https://doi.org/10.3390/wevj16070385.

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The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic

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Wang, Yang, and Xiaoxiang Liang. "Application of Reinforcement Learning Methods Combining Graph Neural Networks and Self-Attention Mechanisms in Supply Chain Route Optimization." Sensors 25, no. 3 (2025): 955. https://doi.org/10.3390/s25030955.

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Optimizing transportation routes to improve delivery efficiency and resource utilization in dynamic supply chain scenarios is a challenging task. Traditional route optimization methods often struggle with complex supply chain network structures and dynamic changes, which require a more efficient and flexible solution. This study proposes a method that integrates Graph Neural Networks (GNNs), self-attention mechanisms, and meta-reinforcement learning (Meta-RL) in order to address route optimization in supply chains. The goal is to develop a path planning method that excels in both static and dy

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Xu, Yingjie, Xiaobo Tan, Mengxuan Wang, and Wenbo Zhang. "GoalBERT: A Lightweight Named-Entity Recognition Model Based on Multiple Fusion." Applied Sciences 14, no. 23 (2024): 11003. http://dx.doi.org/10.3390/app142311003.

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Named-Entity Recognition (NER) as a core task in Natural Language Processing (NLP) aims to automatically identify and classify specific types of entities from unstructured text. In recent years, the introduction of Transformer architecture and its derivative BERT model has pushed the performance of NER to unprecedented heights. However, these models often have high requirements for computational power and memory resources, making it difficult to train and deploy them on small computing platforms. Although ALBERT as a lightweight model uses parameter sharing and matrix decomposition strategies

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Wu, Jing, Weiyan Zheng, Yanping Jiang, and Yijie Wang. "Design of WPT System Based on Interleaved Boost Converter." Applied Sciences 12, no. 14 (2022): 6994. http://dx.doi.org/10.3390/app12146994.

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With the improvement of technology, the demand for electrical power continues to deepen. Wireless Power Transfer (WPT) technology can transmit power without using physical media such as cables, and it has the advantages of electrical isolation, convenience, and safety. At present, the miniaturization of the secondary side is an emerging trend of WPT systems, which is analyzed in this paper. By introducing an interleaved boost converter in the front stage, the DC bus voltage of the primary side is increased, the loss of the primary side is reduced, and the system efficiency is improved. At the

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Jin, Yucheng, Wei Shen, and Chase Q. Wu. "Power Prediction Based on Signal Decomposition and Differentiated Processing with Multi-Level Features." Electronics 14, no. 10 (2025): 2036. https://doi.org/10.3390/electronics14102036.

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As global energy demand continues to rise, accurate load forecasting has become increasingly crucial for power system operations. This study proposes a novel Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Fast Fourier Transform-inverted Transformer-Long Short-Term Memory (CEEMDAN-FFT-iTransformer-LSTM) methodological framework to address the challenges of component complexity and transient fluctuations in power load sequences. The framework initiates with CEEMDAN-based signal decomposition, which dissects the original load sequence into multiple intrinsic mode functions (IM

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Li, Dong, Panfei Yang, and Yuntao Zou. "Optimizing Insulator Defect Detection with Improved DETR Models." Mathematics 12, no. 10 (2024): 1507. http://dx.doi.org/10.3390/math12101507.

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With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detection of defects in insulators captured under challenging conditions, such as rain, snow, fog, sunlight, and fast-moving drones during long-distance photography, remains a major challenge. To address this issue and improve the accuracy of defect detection, this paper presents a novel approach: the Multi-Scale Insulator Defect Detection Approach using D

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Liu, Fengyu, Jinhe Chen, Jun Yu, and Rui Zhong. "Next Point of Interest (POI) Recommendation System Driven by User Probabilistic Preferences and Temporal Regularities." Mathematics 13, no. 8 (2025): 1232. https://doi.org/10.3390/math13081232.

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The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent services. However, existing POI recommendation systems face three major challenges: (1) oversimplification of user preference modeling, limiting adaptability to dynamic user needs, (2) lack of explicit arrival time modeling, leading to reduced accuracy in time-sensitive scenarios, and (3) complexity in trajectory representation and spatiotemporal m

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Reis, Manuel J. C. S., and Carlos Serôdio. "Edge AI for Real-Time Anomaly Detection in Smart Homes." Future Internet 17, no. 4 (2025): 179. https://doi.org/10.3390/fi17040179.

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The increasing adoption of smart home technologies has intensified the demand for real-time anomaly detection to improve security, energy efficiency, and device reliability. Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. This paper presents an Edge AI-based anomaly detection framework that combines Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) models to identify anomalies in IoT sensor data. The system is evaluated on both synthetic and real-w

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Giaconia, Cettina, and Aziz Chamas. "MySTOCKS: Multi-Modal Yield eSTimation System of in-prOmotion Commercial Key-ProductS." Computation 13, no. 3 (2025): 67. https://doi.org/10.3390/computation13030067.

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In recent years, Out-of-Stock (OOS) occurrences have posed a persistent challenge for both retailers and manufacturers. In the context of grocery retail, an OOS event represents a situation where customers are unable to locate a specific product when attempting to make a purchase. This study analyzes the issue from the manufacturer’s perspective. The proposed system, named the “Multi-modal yield eSTimation System of in-prOmotion Commercial Key-ProductS” (MySTOCKS) platform, is a sophisticated multi-modal yield estimation system designed to optimize inventory forecasting for the agrifood and la

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Wang, Song, Jianxia Xiang, Daqing Chen, and Cong Zhang. "A Method for Detecting Tomato Maturity Based on Deep Learning." Applied Sciences 14, no. 23 (2024): 11111. http://dx.doi.org/10.3390/app142311111.

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In complex scenes, factors such as tree branches and leaves occlusion, dense distribution of tomato fruits, and similarity of fruit color to the background color make it difficult to correctly identify the ripeness of the tomato fruits when harvesting them. Therefore, in this study, an improved YOLOv8 algorithm is proposed to address the problem of tomato fruit ripeness detection in complex scenarios, which is difficult to carry out accurately. The algorithm employs several technical means to improve detection accuracy and efficiency. First, Swin Transformer is used to replace the third C2f in

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Li, Xuan, Mengyuan Yu, Dihong Xu, Shuhong Zhao, Hequn Tan, and Xiaolei Liu. "Non-Contact Measurement of Pregnant Sows’ Backfat Thickness Based on a Hybrid CNN-ViT Model." Agriculture 13, no. 7 (2023): 1395. http://dx.doi.org/10.3390/agriculture13071395.

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Backfat thickness (BF) is closely related to the service life and reproductive performance of sows. The dynamic monitoring of sows’ BF is a critical part of the production process in large-scale pig farms. This study proposed the application of a hybrid CNN-ViT (Vision Transformer, ViT) model for measuring sows’ BF to address the problems of high measurement intensity caused by the traditional contact measurement of sows’ BF and the low efficiency of existing non-contact models for measuring sows’ BF. The CNN-ViT introduced depth-separable convolution and lightweight self-attention, mainly con

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Zhang, Xinyi, Yawen Wu, Peipei Zhou, Xulong Tang, and Jingtong Hu. "Algorithm-hardware Co-design of Attention Mechanism on FPGA Devices." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–24. http://dx.doi.org/10.1145/3477002.

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Multi-head self-attention (attention mechanism) has been employed in a variety of fields such as machine translation, language modeling, and image processing due to its superiority in feature extraction and sequential data analysis. This is benefited from a large number of parameters and sophisticated model architecture behind the attention mechanism. To efficiently deploy attention mechanism on resource-constrained devices, existing works propose to reduce the model size by building a customized smaller model or compressing a big standard model. A customized smaller model is usually optimized

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Manashev, I. R., T. O. Gavrilova, I. M. Shatokhin, M. Kh Ziatdinov, and L. I. Leont’ev. "Utilization of dispersed waste of ferroalloy production on the basis of metallurgical SHS-process." Izvestiya. Ferrous Metallurgy 63, no. 8 (2020): 591–99. http://dx.doi.org/10.17073/0368-0797-2020-8-591-599.

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A review of methods for processing dispersed waste from f loy production was performed. In ferroalloy plants there is a problem of formation and accumulation of cyclone dust (the catch product from crushing – CPC), formed during the grinding and fractionation of ferroalloys. The drawbacks of the known methods for the disposal of such dust are shown. The authors have investigated the possibility of obtaining commercial nitrided ligatures from CPC’s and substandard ferroalloy fines using the technology of self-propagating high-temperature synthesis (SHS). On the basis of the proposed “metallurgi

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Li, Guanzhou, Jianping Wu, Yujing He, and Duowei Li. "CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation." Applied Sciences 13, no. 20 (2023): 11257. http://dx.doi.org/10.3390/app132011257.

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Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural ne

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Lai, Tin. "Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care." BioMedInformatics 4, no. 1 (2024): 113–26. http://dx.doi.org/10.3390/biomedinformatics4010008.

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Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’

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Martin Louis. "Personalized recommendation systems for customer self-service and promotions: Enhancing effortless customer experience." World Journal of Advanced Research and Reviews 11, no. 3 (2021): 509–26. https://doi.org/10.30574/wjarr.2021.11.3.0391.

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The growing demand for seamless and personalized customer experiences has transformed how businesses approach self-service and promotional strategies. This research explores implementing customized recommendation systems to enhance customer engagement, satisfaction, and loyalty across various industries. By leveraging advanced algorithms and customer data, these systems enable businesses to offer tailored solutions that meet individual preferences, streamline self-service interactions, and improve promotional effectiveness. Through surveys, experiments, and case studies, the study highlights t

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Dallakyan, Armine, Naira Martirosyan, and Karine Shakarian. "COACHING ACTIVITY AND ITS PRACTICAL SIGNIFICANCE IN THE PEDAGOGICAL PROCESS." Main Issues Of Pedagogy And Psychology 22, no. 2 (2022): 56–65. http://dx.doi.org/10.24234/miopap.v22i2.442.

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The article analyzes the peculiarities of coaching as pedagogical technology. That activity is presented in the context of a coach?s internship. Pedagogical science is the key to the development of modern society, which implies that the education system must be transformed in line with the changes of time. Coaching as a pedagogical technology ensures the learner's personal growth based on planning his own strategy. Therefore, the use of coaching as a pedagogical technology is relevant and in demand in modern schools, as it reveals the maximum potential of all learners and helps them develop co

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Dallakyan, Armine, Naira Martirosyan, and Karine Shakarian. "COACHING ACTIVITY AND ITS PRACTICAL SIGNIFICANCE IN THE PEDAGOGICAL PROCESS." Main Issues Of Pedagogy And Psychology 9, no. 2 (2022): 56–65. http://dx.doi.org/10.24234/miopap.v9i2.442.

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The article analyzes the peculiarities of coaching as pedagogical technology. That activity is presented in the context of a coach?s internship. Pedagogical science is the key to the development of modern society, which implies that the education system must be transformed in line with the changes of time. Coaching as a pedagogical technology ensures the learner's personal growth based on planning his own strategy. Therefore, the use of coaching as a pedagogical technology is relevant and in demand in modern schools, as it reveals the maximum potential of all learners and helps them develop co

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Jakimow, Tanya. "DECENTRALISED GOVERNANCE AS SITES FOR SELF-FORMATION: A COMPARISON OF PRACTICES OF WELFARE DISTRIBUTION IN TELANGANA, INDIA, AND CENTRAL LOMBOK, INDONESIA." International Journal of Asian Studies 11, no. 2 (2014): 161–85. http://dx.doi.org/10.1017/s1479591414000151.

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Studies that examine the effects of decentralisation for social change or stasis have placed necessary attention on its institutional dynamics: the ways social institutions have transformed as a result of new governance regimes, or alternatively, how the existing institutional context and attendant power relations determine its actualisation. The second facet of the structure/agency dialectic is often overlooked however, that is, the actors themselves. This article seeks to overcome this lacuna by exploring the effects of citizens' engagement in practices associated with decentralised governan

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Abrari, Fuad, and I. Made Sukresna. "Pengaruh Self Congruity, Perceived Price, Brand Trust, dan Customer Engagement terhadap Willingness to Continue and Subscribe pada Disney Plus Hotstar." Ekonomis: Journal of Economics and Business 8, no. 2 (2024): 1200. http://dx.doi.org/10.33087/ekonomis.v8i2.1601.

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The advancement of online streaming media and the growing interest of the Indonesian population in Video on Demand (VoD) applications have paralleled the development of the internet in Indonesia. The presence of various video streaming platforms with local, regional, and global reach operating in Indonesia has generated significant dynamics in the digital entertainment industry. This phenomenon has rapidly transformed the landscape, fostering the adoption of technology and altering consumer behavior patterns in accessing entertainment content. This research is aimed at uncovering the relations

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Giroux, Henry A. "Thinking Dangerously: The Role of Higher Education in Authoritarian Times." Chowanna 54, no. 1 (2020): 1–12. http://dx.doi.org/10.31261/chowanna.2020.54.03.

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The notion of a neutral, objective education is an oxymoron. Education and pedagogy do not exist outside of ideology, values and politics. Ethics, when it comes to education, demand an openness to the other, a willingnessto engage a “politics of possibility” through a continual critical engagement with texts, images, events and other registers of meaning as they are transformed into pedagogical practices both within and outside of the classroom. Education is never innocent: It is always implicated in relations of power and specific visions of the present and future. This suggests the need for

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Barkhatova, Daria A., Lyudmila B. Khegay, and Nikolai I. Pak. "Pedagogical design of “inverted” learning resources for home study." Perspectives of Science and Education 60, no. 6 (2022): 244–62. http://dx.doi.org/10.32744/pse.2022.6.14.

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Introduction. Self-education is becoming increasingly important in terms of the concept of lifelong learning. The changes in the ways of perceiving information, along with the new tendencies in the formation of “engaging” Internet content, cause a necessity to find new methods of presenting educational material aimed at efficient learning and meeting differing requirements of end users. The new demands of today’s young people on the self-education process actualise the need to describe the pedagogical design of “inverted” resource-transformers oriented towards productive self-learning in home

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