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Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2024年 17卷 1286664页

作者： Bhutta, M. Raheel Ali, Muhammad Umair Zafar, Amad Kim, Kwang Su Byun, Jong Hyuk Lee, Seung Won Univ UTAH Asia Campus Dept Elect & Comp Engn Incheon South Korea Sejong Univ Dept Intelligent Mechatron Engn Seoul South Korea Pukyong Natl Univ Dept Sci Comp Busan South Korea Nagoya Univ Grad Sch Sci Div Biol Sci Interdisciplinary Biol Lab iBLab Nagoya Japan Pusan Natl Univ Inst Math Sci Dept Math Busan South Korea Pusan Natl Univ Finace Fishery Manufacture Ind Math Ctr BigData Busan South Korea Sungkyunkwan Univ Dept Precis Med Sch Med Suwon South Korea

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject's forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer-Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.

关键词： spontaneous lie detection deception deep learning algorithm functional near-infrared spectroscopy (fNIRS) classification

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Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis

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BIOSENSORS & BIOELECTRONICS 2024年 263卷 116629页

作者： Lim, Hyung-Jun Kim, Gye Wan Heo, Geon Hyeock Jeong, Uidon Kim, Min Jeong Jeong, Dokyung Hyun, Yoonsuk Kim, Doory Hanyang Univ Dept Chem Seoul 04763 South Korea Inha Univ Dept Math Incheon 22212 South Korea Hanyang Univ Res Inst Convergence Basic Sci Inst Nano Sci & Technol Seoul 04763 South Korea Hanyang Univ Res Insititute Nat Sci Seoul 04763 South Korea

The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the single particle level faces challenges owing to their small size and the complexity of biological fluids. This new vesicle analysis tool leverages the single-molecule sensitivity of super-resolution microscopy (SRM) and the high-throughput analysis capability of deep-learning algorithms. By comparing classical clustering methods (k-means, DBSCAN, and SR-Tesseler) with deep-learning-based approaches (YOLO, DETR, Deformable DETR, and Faster R-CNN) for the analysis of super-resolution fluorescence images of exosomes, we identified the deep-learning algorithm, Deformable DETR, as the most effective. It showed superior accuracy and a reduced processing time for detecting individual vesicles from SRM images. Our findings demonstrate that image-based deep-learning-enhanced methods from SRM images significantly outperform traditional coordinate-based clustering techniques in identifying individual vesicles and resolving the challenges related to misidentification and computational demands. Moreover, the application of the combined Deformable DETR and ConvNeXt-S algorithms to differently labeled exosomes revealed its capability to differentiate between them, indicating its potential to dissect the heterogeneity of vesicle populations. This breakthrough in vesicle analysis suggests a paradigm shift towards the integration of AI into super-resolution imaging, which is promising for unlocking new frontiers in vesicle biology, disease diagnostics, and the development of vesicle-based therapeutics.

关键词： Super-resolution fluorescence microscopy Cluster analysis Exosome deep learning algorithm

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Assessment of Photovoltaic Capabilities in Urban Environments: A Case Study of Islamabad, Pakistan

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2020年第6期142卷 061006页

作者： Zubair, Muhammad Ghuffar, Sajid Shoaib, Muhammad Awan, Ahmed Bilal Bhatti, Abdul Rauf Majmaah Univ Coll Engn Dept Elect Engn Riyadh 11952 Saudi Arabia Inst Space Technol Dept Space Sci Islamabad 44000 Pakistan Govt Coll Univ Dept Elect Engn Faisalabad 38000 Pakistan

Photovoltaic (PV) estimation in an urban environment requires detection of rooftop area, design of PV system based on optimization on PV placement distance and the study of additional benefit of lower cooling load of building by shading provided by PV panels. The study is aimed at policymakers to introduce renewable energy policy toward net-zero energy buildings in urban areas. In this research, the capital city of Pakistan, Islamabad, is analyzed for rooftop PV capabilities using deep learning algorithms. The area of the rooftop is calculated by extracting buildings in high-resolution satellite imagery using a deep learning algorithm. The site location is analyzed for available solar energy resources. The distance between the rooftop-PV array is optimized based on self-shading losses, coefficient of performance, energy yield, net-zero energy analysis, and reduction of cooling load of the building provided by PV arrays as shading devices. The 40-km(2) area of Islamabad considered in this research can generate 1038 GWh of solar energy annually from its 4.3-km(2) rooftop area by installed capacity of 447 MW PV panels rows placed at 0.75 m apart. The electricity generated by Islamabad can curtail residential load from the national grid and form a near net-zero energy zone while the electrical energy from the grid can be provided to the industries to enhance the economy and reduce unemployment in Pakistan.

关键词： deep learning algorithm photovoltaic arrays energy analysis building energy GIS

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Bridge Inspection and condition assessment using Unmanned Aerial Vehicles (UAVs): Major challenges and solutions from a practical perspective

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SMART STRUCTURES AND SYSTEMS 2019年第5期24卷 669-681页

作者： Jung, Hyung-Jo Lee, Jin-Hwan Yoon, Sungsik Kim, In-Ho Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Daejeon 34141 South Korea

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amou

关键词： bridge inspection unmanned aerial vehicle (UAV) imaging device condition assessment deep learning algorithm

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Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System

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ACS SENSORS 2021年第11期6卷 4167-4175页

作者： Liu, Hongyu Meng, Gang Deng, Zanhong Nagashima, Kazuki Wang, Shimao Dai, Tiantian Li, Liang Yanagida, Takeshi Fang, Xiaodong Shenzhen Technol Univ Coll New Mat & New Energies Shenzhen 518118 Peoples R China Shenzhen Univ Sch Biomed Engn Guangdong Key Lab Biomed Measurements & Ultrasoun Hlth Sci Ctr Shenzhen 518060 Peoples R China Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei 230031 Peoples R China Chinese Acad Sci Key Lab Photovolta & Energy Conservat Mat Hefei 230031 Peoples R China Univ Tokyo Grad Sch Engn Dept Appl Chem Tokyo 1138656 Japan Soochow Univ Sch Phys Sci & Technol Jiangsu Key Lab Thin Films Ctr Energy Convers Mat & Phys CECMP Suzhou 215006 Peoples R China

Discriminating structurally similar volatile organic compounds (VOCs) molecules, such as benzene, toluene, and three xylene isomers (BTX), remains a significant challenge, especially, for metal oxide semiconductor (MOS) sensors, in which selectivity is a long-standing challenge. Recent progress indicates that temperature modulation of a single MOS sensor offers a powerful route in extracting the features of adsorbed gas analytes than conventional isothermal operation. Herein, a rectangular heating waveform is applied on NiO-, WO3-, and SnO(2)(-)based sensors to gradually activate the specific gas/oxide interfacial redox reaction and generate rich (electrical) features of adsorbed BTX molecules. Upon several signal preprocessing steps, the intrinsic feature of BTX molecules can be extracted by the linear discrimination analysis (LDA) or convolutional neural network (CNN) analysis. The combination of three distinct MOS sensors noticeably benefits the recognition accuracy (with a reduced number of training iterations). Finally, a prototype of a smart BTX recognition system (including sensing electronics, sensors, Wi-Fi module, UI, PC, etc.) based on temperature modulation has been explored, which enables a prompt, accurate, and stable identification of xylene isomers in the ambient air background and raises the hope of innovating the future advanced machine olfactory system.

关键词： BTX molecules xylene isomer classification temperature modulation deep learning algorithm smart sensing system

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An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8170-8183页

作者： Jia, Dongyao Chen, Haibo Zheng, Zuduo Watling, David Connors, Richard Gao, Jianbing Li, Ying Univ Queensland Sch Civil Engn Brisbane Qld 4072 Australia Univ Leeds Inst Transport Studies Leeds LS2 9JT W Yorkshire England Dynnoteq Ltd London HR5 3DJ England

The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-hoard, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated.

关键词： Predictive cruise control cloud-based system model predictive control traffic prediction deep learning algorithm

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Autonomous balance control method of unmanned aerial vehicle with manipulator based on artificial intelligence algorithm

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SOFT COMPUTING 2023年第23期28卷 13897-13907页

作者： Li, Qinlin Xing, Dan Ilyas, M. A. Kamarudin, Nazhatul Hafizah Chengdu Coll Univ Elect Sci & Technol China Fac Engn Chengdu 611731 Sichuan Peoples R China UCSI Univ Fac Engn Technol & Built Environm Dept Elect & Elect Engn Kuala Lumpur 56000 Malaysia Hebei Inst Commun Informat Technol & Cultural Management Inst Shijiazhuang 051430 Hebei Peoples R China

With the continuous development of society, the universal applicability of Unmanned Aerial Vehicles (UAVs) has gradually emerged, because the facilities, such as surveillance cameras, lifting ropes and manipulators that can be modified are widely used in various fields, and are engaged in various dangerous work beyond the reach of human resources. It is not hard to imagine that in the near future, UAVs would inevitably join in more complex tasks. While UAVs have unique advantages, they also need to pay more attention to maintenance and care to ensure the stability of UAVs in the process of performing tasks and avoid the occurrence of a crash caused by imbalance. For this reason, this paper would set up an automatic balance control method based on Artificial Intelligence (AI) algorithm to study the balance of UAVs with manipulators. Through comparative study with the balance method based on deep learning algorithm, it was found that the method based on AI algorithm can help UAVs better maintain balance. The detection accuracy of the method studied in this paper for UAV flight balance stability was above 96%, and the detection accuracy of UAV automatic balance stability based on deep learning algorithm was below 92%. At the same time, in the face of different influencing factors, the UAV based on artificial intelligence algorithm can also maintain the balance of flight faster and maintain the stability of landing and development flight. Therefore, the research in this paper is meaningful.

关键词： Autonomous balance control Equipped with manipulator Unmanned aerial vehicle Artificial intelligence algorithm deep learning algorithm

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Research on the commonness and dissimilarity of group machine tools based on BP and SAE algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 158卷

作者： Yuan, Xuwu Hao, Caihua Gu, Jiarong Mao, Xinyong Liu, Hongqi Zhang, Jie Jiang, Xuchu Zhu, Hongguang Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Wuhan Intelligent Equipment Ind Technol Inst Co L Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Hubei Peoples R China Zhongnan Univ Econ & Law Sch Stat & Math Wuhan 430074 Hubei Peoples R China CSIC 722 Res & Dev Inst Wuhan 430079 Peoples R China

The use of group machine tools has become one of the important production methods for large-scale manufacturing of high-precision parts. Due to the impact and wear of the group machine tool for long-term machining, the dynamic characteristics of the machine tool structure change significantly, resulting in a decrease in product quality;at the same time, the random errors in manufacturing and assembly make the group machine tool structure dynamics more different. Studying the dynamic differences of group machine tools will help improve the processing of group machine tools. This paper proposes a deep learning algorithm based on Back-propagation (BP) neural network and Stacked Auto Encoder (SAE) network to study the dynamics of the machine tool structure under static and dynamic conditions. The BP neural network is used to train the characteristics of some machine tools, and the dynamic characteristics of other groups of machine tools are predicted by changing the parameters. Then SAE network is used to find out the influence rule of group machine tool structure, changing on group machine tool dynamic characteristics. Experiments have proved that this method has certain significance for the study of dynamic characteristics of group machine tools.

关键词： Group machine tools Machine tool dynamics deep learning algorithm

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Development of a deep Neural Network-based Life Accident Evaluation Model for Weather-related Railway Accidents

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第10期28卷 4624-4638页

作者： Kim, Ji-Myong Adhikari, Manik Das Yum, Sang-Guk Mokpo Natl Univ Dept Architectural Engn Mokpo 58554 South Korea Gangneung Wonju Natl Univ Dept Civil & Environm Engn Kangnung 25457 South Korea

Global warming worldwide is the reason for the increasing number of meteorological disasters causing severe property damage and human casualties. Railways are a key social infrastructure;however, quantitative and empirical research into the impact of weather changes due to global warming has not been done adequately. Thus, this study aims to develop a predictive model using a deep learning algorithm to quantify the relationship between fatal rail accidents and weather conditions. The proposed framework utilizes the deep Neural Network (DNN) technique trained with past rail accidents and weather data. The model performance was evaluated using error metrics (mean absolute error (MAE) and root-mean-square error (RMSE)) and compared with widely used regression techniques. The findings showed that the DNN model achieved lower RMSE and MAE compared to the multi-regression, random forest and support vector machine models, with a reduction in prediction error ranging from 1.04% to 20.78% in RMSE and 5.0% to 15.3% in MAE. This exhibits the DNN model's effectiveness in capturing complex relationships within the data and delivering more accurate predictions compared to the other models. The approach and outcomes of this study provide essential guidelines for the efficient and safe maintenance and optimized safety management of railway services.

关键词： Climate change Railroad accident Casualty deep learning algorithm Accuracy

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deep learning application in diverse fields with plant weed detection as a case study 21

Deep learning application in diverse fields with plant weed ...

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Proceedings of the International Conference on Artificial Intelligence and its Applications

作者： Uchechi F. Ukaegbu Lagouge K. Tartibu Modestus O. Okwu Isaac O. Olayode Department of Mechanical & Industrial Engineering University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9781450385756

Machine learning applications have gained popularity over the years as more advanced algorithms like the deep learning (DL) algorithm are being employed in signal identification, classification and detection of cracks or faults in structures. The DL algorithm has broader applications compared to other machine learning systems and it is a creative algorithm capable of processing data, creating pattern, interpreting information due to its high level of accuracy in pattern recognition under stochastic conditions. This research gives an exposition of DL in diverse areas of operations with a focus on plant weed detection which is inspired by the need to treat a specific class of weed with a particular herbicide. A Convolutional Neural Network (CNN) model was trained through transfer learning on a pre-trained ResNet50 model and the performance was evaluated using a random forest (RF) classifier, the trained model was deployed on a raspberry pi for prediction of the test data. Training accuracies of 99% and 93% were obtained for the CNN and RF classifier respectively. Some recommendations have been proffered to improve inference time such as the use of better embedded systems such as the Nvidia Jetson TX2, synchronizing DL hardware accelerators with appropriate optimization techniques. A prospect of this work would be to incorporate an embedded system, deployed with DL algorithms, on an unmanned aerial vehicle or ground vehicle. Overall, it is revealed from this study that DL is highly efficient in every sector and can improve the accuracy on automatic detection of systems in especially in this era of Industry 4.0.

关键词： Random forest fourth industrial revolution deep learning algorithm Agriculture Convolutional neural network

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