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IEEE Conference on Nanotechnology

作者： Zhen Gao Lini Yuan Pedro Reviriego Shanshan Liu Fabrizio Lombardi School of Electrical and Information Engineering Tianjin University Tianjin China ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan China Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

Stable Diffusion is a popular Transformer-based model for image generation from text; it applies an image information creator to the input text and the visual knowledge is added in a step-by-step fashion to create an image that corresponds to the input text. However, this diffusion process can be corrupted by errors from the underlying hardware, which are especially relevant for implementations at the nanoscales. In this paper, the dependability of Stable Diffusion is studied focusing on soft errors in the memory that stores the model parameters; specifically, errors are injected into some critical layers of the Transformer in different blocks of the image information creator, to evaluate their impact on model performance. The simulations results reveal several conclusions: 1) errors on the down blocks of the creator have a larger impact on the quality of the generated images than those on the up blocks, while the errors on middle block have negligible effect; 2) errors on the self-attention (SA) layers have larger impact on the results than those on the cross-attention (CA) layers; 3) for CA layers, errors on deeper levels result in a larger impact; 4) errors on blocks at the first levels tend to introduce noise in the image, and those on deep layers tend to introduce large colored blocks. These results provide an initial understanding of the impact of errors on Stable Diffusion.

关键词： Image quality Visualization Image synthesis Image color analysis Simulation Noise Diffusion processes

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International Electrical Engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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From Detection to Traversal: a Probabilistic Framework for UAS-Assisted Landmine Mapping and Circumvention

From Detection to Traversal: a Probabilistic Framework for U...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： J. Josiah Steckenrider Dongbin Kim Pratheek Manjunath Jack Simpson Charles Rosbrook Micah Weiss Department of Civil and Mechanical Engineering United States Military Academy West Point NY USA College of Engineering Technology and Architecture University of Hartford West Hartford CT USA Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This research presents a robust probabilistic framework for minefield localization, mapping, and avoidance, addressing a technological gap in the field of aerial countermine intelligence, while bypassing the well-established techniques of landmine detection. Our approach propagates the pose uncertainty matrix delivered by a drone's flight controller's Kalman filter to probabilistically estimate the location of detected mines. This probability map then seeds an artificial potential field path generator which creates a safe path for ground traversal by producing waypoints through the minefield. The system's performance is evaluated in simulations and validated through flight trials, demonstrating its potential to improve the efficiency and safety of UAV-assisted minefield navigation and threat avoidance.

关键词： Location awareness Landmine detection Uncertainty System performance Atmospheric modeling Probabilistic logic Aircraft navigation Generators Safety Kalman filters

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Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

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Computational Intelligence and Neuroscience 2022年第0期2022卷 3216400页

作者： Hosseini Nejad Takhti, Ali Saffari, Abbas Martín, Diego Khishe, Mohammad Mohammadi, Mokhtar Department of Information Technology College of Engineering and Computer Science Sari Branch Islamic Azad University Sari Iran Department of Electrical Engineering Imam Khomeini Marine Science University Nowshahr Iran ETSI Telecomunicación Universidad Politécnica de Madrid Av. Complutense 30 Madrid28040 Spain Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms. © 2022 Ali Hosseini Nejad Takhti et al.

关键词： Fuzzy inference

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

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Journal of computer science & technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

arXiv

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arXiv 2022年

作者： Wang, Ran Zhao, Yifei Wang, Shipan Du, Kehan Tang, Mojie Li, Yiyao Chen, Qianhe Tao, Hongxi Wang, Yong School of Journalism and Information Communication Huazhong University of Science and Technology China School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Computer Science United Kingdom University of Bristol United Kingdom School of Architecture and Urban Planning Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China Computer Science Department University College London United Kingdom School of Computing and Information Systems Singapore Management University Singapore

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. RumorLens integrates natural language processing (NLP) and other data processing techniques with visualization techniques to facilitate interactive analysis and validation of suspected rumors. We propose well-coordinated visualizations to provide users with three levels of details of suspected rumors: an overview displays both spatial distribution and temporal evolution of suspected rumors;a projection view leverages a metaphor-based glyph to represent each suspected rumor and further enable users to gain a quick understanding of their overall characteristics and similarity with each other;a propagation view visualizes the dynamic spreading details of a suspected rumor with a novel circular visualization design, and facilitates interactive analysis and validation of rumors in a compact manner. By using a real-world dataset collected from Sina Weibo, one case study with a domain expert is conducted to evaluate RumorLens. The results demonstrated the usefulness and effectiveness of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Social networking (online)

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A novel data driven approach for combating energy theft in urbanized smart grids using artificial intelligence

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Expert Systems with Applications 2024年 253卷

作者： Shahzadi, Nazia Javaid, Nadeem Akbar, Mariam Aldegheishem, Abdulaziz Alrajeh, Nabil Bouk, Safdar Hussain Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Urban Planning College of Architecture and Planning King Saud University Riyadh11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Old Dominion University 5115 Hampton Blvd NorfolkVA23529 United States

Electricity Theft (ET) causes monetary losses for power utilities in the energy sector. It occurs when electricity is consumed without being billed. Several methods are available for automatically detecting ET. Most of these methods evaluate Electricity Consumption (EC) records. However, these methods either have a low Detection Rate (DR) or a high deployment cost with a high False Positive Rate (FPR). Moreover, it is difficult to identify fraudulent consumers based solely on EC records. In addition, owing to data imbalances, such methods prove to be inefficient for classification. To solve the aforementioned problems, we have proposed a combination of various techniques. The first one is the Fastfood Transform, which is used for dimensionality reduction, along with the Time Series Lag Embedded Network (TLENET) neural network, used for classification between honest and dishonest consumers. The second one is the Wavelet Transform used for dimensionality reduction with TLENET, and the third one is the Nyström method used for dimensionality reduction with TLENET. To tackle the risk of high variance that results in overfitting in Deep Learning (DL) models, a Localized Random Affine Shadowsampling (LoRAS) data balancing technique is used. We have employed various data balancing techniques to analyze the performance of our system model. A game theory based approach, SHapley Additive exPlanations (SHAP), is implemented for explaining the output of our deep model. We have used a real-world dataset, referred as the State Grid Corporation of China (SGCC), to perform the simulations. Our model has achieved 94% accuracy, 92% F1-score, 93% Area Under Curve-Receiver Operating Characteristics (AUC-ROC), and 87% Matthews Correlation Coefficient (MCC) with LoRAS, Wavelet Transform, and TLENET. With dimensionality reduction using Fastfood Transform, our model has achieved 93% accuracy, 92% F1-score, 92% AUC-ROC, and 85% MCC. When the Nyström method is employed for dimensionality redu

关键词： Wavelet transforms

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Effect of Artificial Roughness on Heat Transfer and Friction Factor in a Solar Air Heater: A Review 2nd

Effect of Artificial Roughness on Heat Transfer and Friction...

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2nd International Conference on Advances in Materials and Manufacturing technology, ICAMMT 2022

作者： Yadav, Anil Singh Alam, Tabish Saxena, Rajiv Yadav, Raj Kumar Kumar, Rajan Sharma, Abhishek Agrawal, Yogesh Viswanath Allamraju, K. Vyas, Monika Singh, Manmohan Chakroborty, Subhendu Mechanical Engineering Department IES College of Technology MP Bhopal462044 India Architecture Planning and Energy Efficiency CSIR-Central Building Research Institute Uttarakhand Roorkee247667 India Infinity Management and Engineering College MP Sagar470001 India Mechanical Engineering Department Adina Institute of Science and Technology MP Sagar470002 India Department of Mechanical Engineering BIT Sindri Jharkhand Dhanbad828123 India Mechanical Engineering Department Sagar Institute of Research and Technology MP Bhopal462041 India Mechanical Engineering Department Institute of Aeronautical Engineering Telangana Hyderabad500043 India Civil Engineering Department IES College of Technology MP Bhopal462044 India Computer Science and Engineering Department IES College of Technology MP Bhopal462044 India Department of Basic Sciences IITM IES University MP Bhopal462044 India

ISBN: (纸本)9789819929207

In order to convert solar energy into thermal energy, solar air heater (SAH) is commonly used. These are used to convert the solar radiation into heat of working fluid. The effect of various geometric constraints on the friction characteristics and heat transfer (HT) of a SAH has been studied. The paper aims to introduce the various parameters that are involved in the analysis of HT enhancement of SAH. There are various techniques that are involved in the analysis of HT enhancement of SAH. One of these is the use of a passive method, which involves the use of turbulators, ribs or fins on the surface. SAHs that are roughened can improve their HT properties compared to their plain counterparts. This is because the various surface patterns and shapes used in their design can help improve their performance. There have been many different sorts of studies conducted on the many facets of these HT augmentation approaches. Studies showed that combining the roughening process with a rough absorber plate can improve the performance of SAH duct. The goal of this study is to identify the most appropriate roughness component for solar air duct that can meet the different operating conditions. The findings of this study can be used to design and construct enhanced SAH duct system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Flow of fluids

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A Memory-Augmented Conditional Neural Process Model for Traffic Prediction

SSRN

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SSRN 2024年

作者： Wei, Ye He, Haitao Yuan, Kunhao Schaefer, Gerald Ji, Zhigang Fang, Hui Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom Centre for Clinical Brain Sciences University of Edinburgh Edinburgh United Kingdom National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong University Shanghai China

This paper presents the first neural process-based model for traffic prediction, the Memory-augmented Conditional Neural Process (MemCNP). Our model effectively addresses key challenges in capturing complex spatio-temporal traffic patterns in data-sparse situations and introduces a novel framework for uncertainty estimation. We further incorporate a novel memory network module designed to acquire a representative contextual reference, thereby improving our model performance under complex data distributions. By integrating the conditional neural process and the memory network, MemCNP enables the learning of the most representative contexts through iterative updates, thus enhancing generalisation capabilities and providing a more adaptable model for a broad range of real-world traffic scenarios, including highway traffic, urban traffic, and non-motorised traffic. This is demonstrated by comprehensive experimental results on six benchmark datasets, PeMS04, PeMS07, PeMS08, NYCTaxi, CHIBike and T-Drive over existing state-of-the-art traffic prediction models, where MemCNP shows superior performance. Additionally, through ablation and reliability studies, we provide a comprehensive analysis of the model's effectiveness. © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Spatio-Temporal Switchable Data Prefetcher for Convolutional Neural Networks

A Spatio-Temporal Switchable Data Prefetcher for Convolution...

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International SoC Design Conference, ISOCC

作者： Jihoon Jang Hyun Kim Hyokeun Lee Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Secure and Advanced Computer Architecture (SACA) Research Group North Carolina State University (NCSU) Raleigh NC USA

In this paper, we propose a spatio-temporal switchable data prefetcher that can adapt to the locality characteristics of CNN models. The proposed prefetcher records the recent delta history by leveraging two tables. The first table predicts spatial address patterns by comparing the delta score with the last delta, while the second table predicts temporal address patterns by recording and reordering the delta sequence from the delta history. Consequently, the proposed prefetcher is capable of appropriately switching between these two prediction methodologies based on spatial and temporal localities. The experimental results on CNN inference workloads show that we achieved high average accuracy of 83.8% and coverage of 81.6%, and hence the proposed prefetcher improves system performance by 33.8% over a baseline with no data prefetcher and 21% over the best-performing prior spatio-temporal prefetcher.

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