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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Tigani, Smail Makrane, Amal Saadane, Rachid Chehri, Abdellah AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School Casablanca20100 Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This research paper presents a deep learning-based predictive model for classifying currency trends using technical indicators. The model is trained on a dataset generated from three technical indicators: relative strength index (RSI), moving average convergence divergence (MACD), and stochastic. The dataset consists of historical currency data along with the corresponding values of the technical indicators. The deep learning model can accurately classify the trends of a given currency based on the importance of these indicators. The model's performance is evaluated using standard metrics, and the results demonstrate its effectiveness in classifying currency trends. The proposed model provides a valuable tool for traders and investors in the foreign exchange market by helping them make informed decisions about the direction of currency prices. © 2023 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)

关键词： Algorithmic Trading Deep Learning Predictive Analytics

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Convolutional Bi-GRU-based prediction model for Climate Indicators

Convolutional Bi-GRU-based prediction model for Climate Indi...

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7th International Conference on Electronics Technology, ICET 2024

作者： Lo, Tsung-Ming Zhang, Hong-Tao He, Yun-Bing Liu, Yi-Zhi Zhang, Wan-Ying Liang, Qiong-Dan Bei, Zhen Dongguan University of Technology Guangdong-Taiwan College of Industrial Science & Technology Dongguan China Guangxi Vocational and Technical College of Communications School of Traffic Information Engineering Nanning China Nanfang College Guangzhou Department of Public administration Guangzhou China Nanfang College-Guangzhou School of Electrical and Computer Engineering Guangzhou China Dongguan University of Technology Intelligent Manufacturing Engineering Dongguan China

ISBN: (纸本)9798350363951

Climate prediction is critical to a wide range of fields, from agriculture to public health, but traditional methods face challenges due to the complexity and time-space characteristics of climate data. Machine learning techniques provide a promising solution by effectively extracting features from climate indicator data. In this study, we propose a new method for predicting climate indicators by combining CNN-Bi-GRU models with FNN networks. Through evaluation, our method demonstrates excellent prediction performance in terms of RMSE and MAE on both test and training sets, and robust convergence behavior is observed in model training. In addition, our method achieves high accuracy in predicting key climate metrics such as temperature and rain frequency, providing valuable information for understanding climate dynamics. Comparison experiments with LSTM-FNN, Double-LSTM, BiLSTM, and Bi-GRU models highlight the superiority of the CNNBi-GRU approach, especially in reducing temperature prediction errors. In conclusion, this study presents a promising approach to climate prediction that can enhance our understanding of climate variability and support informed decision making in various industries. © 2024 IEEE.

关键词： Prediction models

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Adapting a Self-Supervised Speech Representation for Noisy Speech Emotion Recognition by Using Contrastive Teacher-Student Learning 48

Adapting a Self-Supervised Speech Representation for Noisy S...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Leem, Seong-Gyun Fulford, Daniel Onnela, Jukka-Pekka Gard, David Busso, Carlos University of Texas at Dallas Department of Electrical and Computer Engineering United States Boston University Occupational Therapy and Psychological & Brain Sciences United States Harvard University Harvard T.H. Chan School of Public Health Department of Biostatistics United States San Francisco State University Psychology Department United States

ISBN: (纸本)9781728163277

Studies have shown high performance in the speech emotion recognition (SER) task by fine-tuning a self-supervised speech representation model. Although this model can provide emotionally discriminative embedding in clean conditions, adapting it to a noisy target environment is still required when deployed on real-world applications. For adaptation, it is essential to balance between acquiring new knowledge from noisy speech and keeping the previous knowledge acquired during the pre-training and fine-tuning of the model. Therefore, we propose a contrastive teacher-student learning framework to retrain a self-supervised speech representation model for noisy SER. To keep the knowledge of the original model, we minimize the root mean square error between the clean embeddings from the original SER model and the noisy embeddings from the retrained model. To acquire the discriminative knowledge in the target noisy condition, we also minimize the InfoNCE loss by selecting the corresponding clean embedding as a positive sample and other noisy embeddings with different emotional labels as negative samples. Our experiment with the clean and noisy version of the MSP-Podcast corpus demonstrates that the contrastive teacher-student learning framework can significantly improve the performance of the model only trained with the clean speech in the target noisy condition for all the emotional attributes. © 2023 IEEE.

关键词： Embeddings

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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Automotive Seat Surface Defect Detection Method Based on Improved YOLOv5 Algorithm

Automotive Seat Surface Defect Detection Method Based on Imp...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Juchao Zhang Changlin Yu Gang Wang Chunling Xu Zaixiang Pang Zhongbo Sun Department of Control Science and Engineering Changchun University of Technology Changchun China Department of Mechanical and Electrical Engineering Changchun University of Technology Changchun China Department of Public and Basic Computer Education Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

A method for detecting surface defects of car seats based on improved YOLOv5 is proposed to address the current issues of low efficiency, high missed detection rate, and difficult cost control in manual inspection of surface defects of car seats. In addition, in order to solve the imbalance problem of various defect images collected by industrial cameras, the methods of translation, cropping and adding noise are used to pre-enhance the images. Train the manually annotated dataset using an improved Yolov5 model, which uses K-means++ clustering anchor boxes to enhance adaptability to the dataset in this paper. At the same time, add a BiFormer module to the network to reduce the loss of small target defects and enhance the network’s feature fusion and extraction capabilities. The experimental results show that the mAP of the whole is increased from 67.1% to 78.2%, and the model can meet the requirements of the identification of car seat surface defects.

关键词： YOLO Deep learning Training Machine vision Noise Inspection Feature extraction

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Circularly Polarized Wideband Antenna for 5G Millimeter Wave Application

Circularly Polarized Wideband Antenna for 5G Millimeter Wave...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Hussain, Musa Abbas, Saqlain Alibakhshikenari, Mohammad Dalarsson, Mariana Falcone, Francisco Bahria University Deaprtment of Electrical Engineering Islamabad Campus Pakistan Virtual University of Pakistan Department of Computer Science Lahore Pakistan Universidad Carlos III de Madrid Department of Signal Theory and Communications Leganés Madrid28911 Spain KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm100-44 Sweden Public University of Navarre Electric Electronic and Communication Engineering Department Pamplona31006 Spain Public University of Navarre Institute of Smart Cities Pamplona31006 Spain

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

In this work, a novel millimeter-wave circularly polarized patch antenna is presented. The antenna offers wide bandwidth, high gain, compact size, simple geometry, low profile and wide axial ratio. The proposed antenna is designed on the top side of Rogers RT/duroid 6002 with thickness of 0.79mm. The analysis and numerical simulation of the proposed antenna is performed using the full-wave electromagnetic simulator, HFSS. © 2022 IEEE.

关键词： Microstrip antennas

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Freight Routing for System Efficiency and Sustainability

Freight Routing for System Efficiency and Sustainability

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IEEE Forum on Integrated and Sustainable Transportation System (FISTS)

作者： Pengfei Chen Petros Ioannou Maged Dessouky Genevieve Giuliano Department of Electrical Engineering University of Southern California Los Angeles USA Department of Industrial & System Engineering University of Southern California Los Angeles USA Price School of Public Policy University of Southern California Los Angeles USA

The efficient supply of goods and transport of materials are important factors for sustainability in any urban environment where traffic and environmental issues also need to be addressed. In this paper we developed a centrally coordinated approach for routing freight in urban environments where traffic loads are unbalanced in time and space in an effort to improve mobility and reduce cost. We assume that freight is moved by trucks using the road network and truck fleets consist of a mix of diesel and electric trucks. We formulated the routing problem as an optimization problem with several constraints and we use a co-simulation load balancing approach to generate routes for trucks that reduce the overall cost. We use a simulation test of a road network in the Los Angeles/Long Beach Metropolitan areas that includes two major ports to demonstrate the results.

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Effect of Epsilon-Near-Zero Modes on the Casimir Interaction between Ultrathin Films

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Physical Review Letters 2023年第19期130卷 196901-196901页

作者： Tao Gong Iñigo Liberal Benjamin Spreng Miguel Camacho Nader Engheta Jeremy N. Munday Department of Electrical and Computer Engineering University of California Davis California 95616 USA Department of Materials Science and Engineering University of California Davis California 95616 USA Institute of Smart Cities Public University of Navarre Pamplona Navarre 31006 Spain Department of Electronics and Electromagnetism University of Seville Sevilla Sevilla 41012 Spain Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA

Vacuum fluctuation-induced interactions between macroscopic metallic objects result in an attractive force between them, a phenomenon known as the Casimir effect. This force is the result of both plasmonic and photonic modes. For very thin films, field penetration through the films will modify the allowed modes. Here, we theoretically investigate the Casimir interaction between ultrathin films from the perspective of force distribution over real frequencies for the first time. Pronounced repulsive contributions to the force are found due to the highly confined and nearly dispersion-free epsilon-near-zero (ENZ) modes that only exist in ultrathin films. These contributions persistently occur around the ENZ frequency of the film irrespective of the interfilm separation. We further associate the ENZ modes with a striking thickness dependence of a proposed figure of merit (FOM) for conductive thin films, suggesting that the motion of objects induced by Casimir interactions is boosted for deeply nanoscale sizes. Our results shed light on the correlation between special electromagnetic modes and the vacuum fluctuation-induced force as well as the resulting mechanical properties of ultrathin ENZ materials, which may create new opportunities for engineering the motion of ultrasmall objects in nanomechanical systems.

关键词： Casimir effect Electromagnetic interactions Light-matter interaction Metamaterials Optical materials & elements

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A Virtual Farm Tour for public Education about Dairy Industry

A Virtual Farm Tour for Public Education about Dairy Industr...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Anh Nguyen Emma Windfeld Michael Francis Guillaume Lhermie Kangsoo Kim Department of Computer Science University of Calgary The Simpson Centre The School of Public Policy University of Calgary Department of Electrical and Software Engineering University of Calgary The Simpson Centre Faculty of Veterinary Medicine University of Calgary

In this paper, we present an immersive virtual farm simulation developed to provide realistic on-farm experiences to the public. Users could visit the virtual farm, walk through various sites where dairy cows are raised, and learn how the dairy products are produced through the virtual experience. public users' responses about the virtual experience were collected in various measures, e.g., user experience and learning efficacy, via showcases at local libraries. We present preliminary results regarding the potential of the simulation as an effective agricultural education, and discuss our future plans.

关键词： Industries Solid modeling Three-dimensional displays Dairy products Conferences Virtual reality User interfaces

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Plow-Shaped Wideband Antenna for mm-wave 5G Communication

Plow-Shaped Wideband Antenna for mm-wave 5G Communication

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Kiani, Saad Hassan Rafique, Umair Alibakhshikenari, Mohammad Falcone, Fransisco Dalarsson, Mariana IIC University of Technology Department of Electrical Engineering Phnom Penh Cambodia Sapienza University Department of Information Engineering Electronics and Telecommunications Rome00184 Italy Universidad Carlos III de Madrid Department of Signal Theory and Communications Leganés28911 Spain Public University of Navarre Electric Electronic and Communication Engineering Department Pamplona31006 Spain Public University of Navarre Institute of Smart Cities Pamplona31006 Spain KTH Royal Institute of Technology School of Electrical Engineering and Computer Science StockholmSE 100-44 Sweden

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

In this paper, a simple planar plow-shaped wideband antenna is presented for mm-wave 5G devices. The proposed antenna is designed on a thin substrate with an overall size of 10×12 mm2. The results show that the antenna offers 49.5% fractional bandwidth ranging from 22.2 GHz to 36.8 GHz. Moreover, the radiation efficiency of the antenna is >90% throughout the operating bandwidth, with a peak gain of 3.3 dBi. Through its simple structure and high performance characteristics, it can be observed that the proposed antenna can be termed as a potential candidate for future mm-wave 5G communication devices. © 2022 IEEE.

关键词： Radiation efficiency

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