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Strategies for Optimizing Energy Efficiency in Residential Buildings: An Integrated Framework Approach

Strategies for Optimizing Energy Efficiency in Residential B...

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international conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Fanourios Kourougianni Andreas V. Olympios Alexandros Arsalis George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Sustainable design and nearly zero-energy buildings (NZEBs) are essential for managing energy consumption and reducing carbon emissions in the building sector while maintaining occupant comfort. This study focuses on developing an advanced modeling framework for optimizing energy efficiency in residential buildings, integrating computational design, building performance simulation, and multi-objective optimization (MOO). Applied to a typical archetype in Cyprus, the framework evaluates diverse energy-efficient strategies, including innovative measures such as thermochromic glazing, natural ventilation, automated dimming of artificial lighting, and rule-based control electrochromic glazing systems, among other conventional energy efficiency measures (EEMs). MOO considers energy needs and investment costs. Results demonstrate significant energy savings potential, with optimal combinations of EEMs achieving a 77% reduction in cooling demand and 73% in heating demand compared to the baseline. This tool proves valuable for decision-making in both new construction and retrofitting, identifying cost-effective pathways towards NZEBs.

关键词： Heating systems Costs Buildings Energy conservation Decision making Energy measurement Energy efficiency Computational efficiency Optimization Investment

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Utilizing Hybrid-Deep Learning for Autism Spectrum Disorder Detection in Children via Facial Emotion Recognition

Utilizing Hybrid-Deep Learning for Autism Spectrum Disorder ...

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Self Sustainable Artificial Intelligence systems (ICSSAS), international conference on

作者： Jagadesh Balasubramani R Surendran Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that significantly impacts social interaction and communication. Early detection and intervention are crucial for optimal outcomes. This research investigates the use of deep learning techniques to identify ASD based on facial emotion analysis. The proposed approach leverages Self-Attention-Based Progressive Generative Adversarial Networks (SA-PGAN) optimized with the Gorilla Troops Optimizer (GTO) to accurately recognize subtle facial cues associated with ASD. The model is trained on a dataset of facial images from autistic children, undergoing extensive preprocessing to ensure data quality and consistency. The performance of the proposed model is evaluated using various metrics, including accuracy, precision, recall, F1-score, and computational time. The results demonstrate significant improvements over state-of-the-art methods, such as Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). This research contributes to the development of advanced AI-powered tools for early ASD detection, enabling timely interventions and improving the quality of life for individuals with ASD.

关键词： Support vector machines Radio frequency measurement Autism Accuracy Computational modeling Face recognition Generative adversarial networks Convolutional neural networks Random forests

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A Lightweight Generative Adversarial Network for Artistic Style Transfer

A Lightweight Generative Adversarial Network for Artistic St...

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international conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Ping'an Qiao Tongtong He College of Computer Science Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Currently, image style transfer is one of the hotspots in image processing. Generative adversarial networks have been successfully applied in many fields by virtue of their advantages, and the technology of image style transfer has also seen improvements. However, there are still some problems with the existing artistic style transfer models. For example, the generated images will contain noisy textures, the style and color effects are not ideal, and the model has many parameters, which is easy to cause model breakdown and unstable training. To overcome these problems, an image transfer method that improves the cycleconsistent generative adversarial network (CycleGAN) is presented in this paper. According to its structural characteristics, distributed shift convolution (DSConv) is introduced through the quantized neural network, and the reverse residual block is used to optimize the generator network structure, which can greatly reduce the calculation amount of the model while also improving the model's perception ability and accuracy and the quality of the generated images. Experiments show that the proposed approach can improve the content details of the generated image andthe color effect of the style texture, significantly reduces the number of model parameters, reduces the computational cost, improves the transfer rate, upgrading the model performance, and has strong style transfer capabilities.

关键词： Training Image color analysis Convolution Computational modeling Neural networks Generative adversarial networks Knowledge discovery Generators Noise measurement Computational complexity

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On Graph Theory vs. Time-Domain Discrete-Event Simulation for Topology-Informed Assessment of Power Grid Cyber Risk

On Graph Theory vs. Time-Domain Discrete-Event Simulation fo...

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IEEE international conference on Smart Grid Communications (SmartGridComm)

作者： Khandaker Akramul Haque Leen Al Homoud Xin Zhuang Mariam Elnour Ana Goulart Katherine Davis Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Engineering Technology and Industrial Distribution Texas A&M University College Station TX USA

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

The shift toward more renewable energy sources and distributed generation in smart grids has underscored the significance of modeling and analyzing modern power systems as cyber-physical systems (CPS). This transformation has highlighted the importance of cyber and cyber-physical properties of modern power systems for their reliable operation. Graph theory emerges as a pivotal tool for understanding the complex interactions within these systems, providing a framework for representation and analysis. The challenge is vetting these graph theoretic methods and other estimates of system behavior from mathematical models against reality. High-fidelity emulation and/or simulation can help answer this question, but the comparisons have been understudied. This paper employs graph-theoretic metrics to assess node risk and criticality in three distinct case studies, using a Python-based discrete-event simulation called SimPy. Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment. These tools allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.

关键词： measurement Network topology Reliability theory Cyber-physical systems Traffic control Graph theory Smart grids Power system reliability Topology Time-domain analysis

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AttCDCNet: Attention-Enhanced Chest Disease Classification Using X-Ray Images

AttCDCNet: Attention-Enhanced Chest Disease Classification U...

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IEEE SSD international Multi-conference on systems, Signals and Devices

作者： Omar H. Khater Abdullahi S. Shuaibu Sami Ul Haq Abdul Jabbar Siddiqui Computer Engineering Department Computer Engineering Department SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Chest X-rays (X-ray images) have been proven to be effective for the diagnosis of chest diseases, including Pneumonia, Lung Opacity, and COVID-19. However, relying on traditional medical methods for diagnosis from X-ray images is prone to delays and inaccuracies because the medical personnel who evaluate the X-ray images may have preconceived biases. For this reason, researchers have proposed the use of deep learning-based techniques to facilitate the diagnosis process. The preeminent method is the use of sophisticated Convolutional Neural Networks (CNNs). In this work, we introduce an innovative detection model named AttCDCNet for the task of X-ray image diagnosis, enhancing the popular DenseNet121 model by adding an attention block to help the model focus on the most relevant regions, utilizing the focal loss as a loss function to overcome the imbalance of the dataset problem, and utilizing depth-wise convolution to make the model lighter by reducing the parameters. The proposed model demonstrates novelty by effectively integrating these enhancements to address critical challenges in medical image classification, including dataset imbalance and computational efficiency. Through extensive experimental evaluations, the proposed model demonstrates exceptional performance, surpassing the original DenseNet121 and other state-of-the-art models. The proposed model achieved an accuracy, precision and recall of 94.94%, 95.14% and 94.53%, respectively, on the COVID-19 Radiography Dataset. Underscoring its potential to improve diagnostic accuracy and efficiency in real-world medical applications.

关键词： COVID-19 Performance evaluation Accuracy Pneumonia Computational modeling Medical services Personnel X-ray imaging Medical diagnostic imaging Diseases

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Harnessing the power of large language models for effective query resolution and document summarization

Harnessing the power of large language models for effective ...

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international conference on Computational Intelligence and Networks (CINE)

作者： Navya Sonal Agarwal Rajat Kumar Behera School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

Large Language Models (LLMs) are at the forefront of artificial intelligence (AI) research, providing foundational frameworks that power a range of applications from text generation to complex decision support systems. In the digital era, the rapid expansion of applications based on LLMs represents a significant evolution in the way human interact with and manage extensive amount of information. Therefore, this study discusses the development of two innovative LLM based functionalities, Query Master and PDF Summarizer, which leverage LLMs to enhance the efficiency of information consumption and retrieval. Query Master provides an intuitive interface that allows users to ask questions and receive precise, context-relevant answers, thereby simplifying the exploration of complex data. Simultaneously, PDF Summarizer uses LLMs to transform lengthy documents into concise summaries, fulfilling the critical need for rapid and thorough comprehension in both academic and professional contexts. These exemplify the practical benefits of LLM technology in boosting productivity and facilitating informed decision-making in everyday digital interactions. Consequently, it highlights the transformative potential of AI in augmenting human tasks and shaping future innovations in information management and retrieval, demonstrating significant impacts on our workflows and consumption patterns.

关键词： Productivity measurement Decision support systems Technological innovation Large language models Computational modeling Transforms Linguistics Portable document format Information management

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Accuracy and Reliability of a Novel IMU-Based Functional Calibration Algorithm for Clinical 3D Wrist joint Angle Monitoring

Accuracy and Reliability of a Novel IMU-Based Functional Cal...

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Omni-layer Intelligent systems (COINS), international conference on

作者： Alessandro Bonfiglio Elisabetta Farella Raoul Bongers Department of Information Engineering and Computer Science (DISI) University of Trento Trento Italy Euleria Health srl Rovereto Italy Energy Efficient Embedded Digital Architectures (E3DA) Fondazione Bruno Kessler Trento Italy Department of Human Movement Sciences University Medical Center Groningen Gronigen The Netherlands

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Inertial measurement units (IMU) represent a rapid advancement in biomechanical motion analysis. Still, their internal axis reference frame needs to be aligned with the anatomy to provide clinically meaningful metrics. The literature presents several sensor-to-segment calibration algorithms that can serve this purpose. However, due to the lack of wrist-specific calibration algorithms that can be universally applicable in rehabilitation, we aim to present and validate a functional calibration algorithm to monitor 3D wrist joint angles that can fill this gap. We recruited 13 healthy subjects with no sign of musculoskeletal injury and instructed them to perform wrist calibration movements, as well as a sequence of single-plane and multi-plane tasks. We conducted a statistical analysis using statistical parametric mapping (SPM) on continuous joint angle curves and traditional scalar statistical analysis on root mean squared error (RMSE) and range of motion (ROM) to compare the performance of our IMU calibration with an optical motion capture system (OPTO). SPM one-way MANOVA across different movements detected significant differences between OPTO and IMU between 0-20% (p=0.007) and 80-100% (p=0.002) of the movement; however, post-hoc ANOVA with Bonferroni correction did not reach significance (p>0.005). Scalar statistical analysis using one-way MANOVA on RMSE did not detect significant differences between the two systems (p=0.247). The maximum RMSE detected was 15.1° ± 9.4° during a drinking task on the radial/ulnar deviation axis, whereas the minimum was 3.9° ± 5.3° on the internal/external rotation angle during a drawing task. We conclude that our IMU-based FC system can be used as a basis for a wrist joint motion analysis or rehabilitation system that showed no significant differences with respect to a gold standard optical motion analysis system.

关键词： Wrist Accuracy Three-dimensional displays Calibration Optical sensors Task analysis Motion analysis

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Study on the Transient Characteristic measurement System of DC Current Transformer 17

Study on the Transient Characteristic Measurement System of ...

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17th international conference on Future Networks and Communications / 19th international conference on Mobile systems and Pervasive Computing / 12th international conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022

作者： Jin, Qiu Shipu, Wu Bin, Liu China Electric Power Research Institute Wuhan430074 China

Nowadays, thirteen ultra-high voltage direct current (UHVDC) transmission projects have commissioned in China, including Jinping-Sunan, Fulong-Fengxian, Yibin-Jinhua project, and so forth. Meanwhile, another two UHVDC transmission projects are under construction. The highest UHVDC voltage level is ±1100kV. The HVDC transmission is superior in high capacity and long transmission distance, and its control response put forward higher requirements for the transient characteristics of DC current transformer. At present, there is no test method and equipment for testing the transient characteristics of the DC current transformer. Therefore, mastering the transient response characteristics of operating DC current transformer remains to be a challenge. In this paper, a transient test system of DC current transformer is developed, which includes a step current output unit, a high precision acquisition unit and a calibrator algorithm. The system was used to test the rising time, maximum overshoot and transient delay of the DC current transformers installed on the pole line and valve outlet. This study can clarify the influence factors of the transient characteristics of DC transformer on the control and protection system of UHVDC, and can define the principle of selecting the transformer parameters to meet the requirements of the control and protection system of UHVDC. The results of this study have great significance to improve the performance of UHVDC control and protection system. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the conference Program Chairs.

关键词： Electric instrument transformers

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On the Trustworthiness of Face Morphing Attack Detectors

On the Trustworthiness of Face Morphing Attack Detectors

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IEEE international joint conference on Biometrics (IJCB)

作者： Rouqaiah Al-Refai Clara Biagi Cong Chen Kiran Raja Raghavendra Ramachandra Christoph Busch Philipp Terhörst Paderborn University Germany Technical University of Darmstadt Germany Norwegian University of Science and Technology Norway

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Morphing attacks blend face images of multiple distinct identities into a single photo combining their facial characteristics. Since the morphed image can be verified to multiple identities, these attacks greatly threaten face recognition systems. Morphing Attack Detection (MAD), addresses this problem by determining if an image is a bona fide or morphed image. Previous works on MAD focused on the models’ decision to improve their performance and generalizability. However, it has not been investigated whether the estimated probabilities of such decisions accurately reflect the true confidence with which predictions are made. Since these models struggle with unseen attacks, it is important to determine the models’ decision confidence to decide to trust or distrust the decisions. In this work, we (a) demonstrate that state-of-the-art MAD models struggle with providing reliable confidence estimations, (b) propose two new metrics to categorize their confidence prediction behavior, and (c) demonstrate that a simple calibration method can make the model more trustworthy without changing general model performance. The experiments were conducted in cross-dataset evaluation settings across two different MAD models using three different preprocessing and five morphing generation techniques. We found that both models are not calibrated and therefore not trustworthy. Moreover, it is shown that a simple and effective adjustment of the prediction confidence is possible. This will make future presentation attack detection, and especially MAD, models more trustworthy.

关键词： measurement Costs Face recognition Biological system modeling Estimation Detectors Predictive models Calibration Reliability Security

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Talk to Your Brain: Artificial Personalized Intelligence for Emotionally Adaptive AI Interactions

Talk to Your Brain: Artificial Personalized Intelligence for...

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computer Vision and Machine Intelligence (CVMI), international conference on

作者： Sandeep Varma Shivam Shivam Sarun Natarajan Biswarup Ray Bagesh Kumar Om Dabral ZS Associates Pune India Rockwell Automation Pune India Manipal University Jaipur India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

The process of =making an Artificial intelligent system to replicating human emotional understanding and adapt its responses contextually by tailoring its responses based on individual cognitive and emotional states is called Artificial Personalized Intelligence (API). In this paper, we present a finetuned emotionally adaptive AI pipeline capable of generating personalized, human-like responses. Using a custom dataset based on six universal emotions—Sadness, Happiness, Fear, Anger, Surprise, and Disgust—collected from interviews with 30 participants, we have explored two approaches: fine-tuning a LLaMA-3 8B model with the Low-Rank Adapter (LoRA) technique and employing a Retrieval-Augmented Generation (RAG) agent-based framework over the same LLaMA-3 8B model. Emotion classification, prompt engineering, and model fine-tuning were used over the dataset to capture emotional and personalized subtleties in the responses based on different individuals. We conducted a comprehensive analysis, evaluating performance across candidates, emotions, and overall performance, using metrics such as Mean Squared Error (MSE) and Pearson Correlation Coefficient to measure the difference among the outputs from both pipelines and actual human emotional responses. Our results show that prompt engineering combined with LoRA-based fine-tuning significantly enhances the ability to engage in emotionally intelligent and personalized conversations, whereas the RAG-based approach underperformed due to the pretrained LLAMA-3 model’s restrictive neutral and adversarial training. This highlights that fine-tuned LLMs are effective in replicating human emotions for personalized intelligence.

关键词： Training Adaptation models Computational modeling Pipelines measurement uncertainty Brain modeling Prompt engineering Interviews Intelligent systems Machine intelligence

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