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COMPUTING IN SCIENCE & ENGINEERING 2018年第1期20卷 105-106页

作者： Day, Charles Physics Today

It’s not a stretch to compare a soldier’s orders to the program that governs today’s autonomous drones. In those circumstances, the programmer and the person who authorized the program are responsible and culpable ... 详细信息

关键词： Autonomous Aerial Vehicles Criminal Law Military Vehicles Robot Warriors Soldiers Orders Autonomous Drones War Crime Artificial Intelligence Robots Command And Control Systems Military Equipment Military GPU Last Word Artificial Intelligence Weaponized Drones deep learning algorithms

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Damage mode classification in CFRP laminates using convolutional autoencoder and convolutional neural network on acoustic emission waveforms

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年

作者： Krishna, Yelamarthi Sai Raju, Gangadharan Desarkar, Maunendra Sankar Indian Inst Technol Hyderabad Ctr Interdisciplinary Programs Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Mech & Aerosp Engn Hyderabad 502285 Telangana India Indian Inst Technol Hyderabad Dept Artificial Intelligence Hyderabad Telangana India

The acoustic emission (AE) technique is a widely used nondestructive method for in-situ health monitoring of composite structures. Unlike metals, failure mechanisms in composite structures are complex, involving multiple damage modes, and each damage mode has a distinct AE signature. This work uses deep learning algorithms called convolutional autoencoder (CAE) and convolutional neural network (CNN) to classify damage modes in carbon fiber-reinforced polymer laminates using AE waveforms. Tensile experiments are carried out on laminates of various stacking sequences, and the acquired raw AE waveforms are transformed into time-frequency planes called spectrograms using short-time Fourier transform. CAE is used for retrieving deep features associated with damage modes in the latent space from these spectrograms. Subsequently, k-means is used to cluster the deep features in the latent space. Each cluster is labeled with a damage mode by inspecting their damage signatures using the scalograms. This labeled data is then used to train the CNN. The CNN, once trained is used on the AE data of pristine and notched quasi-isotropic specimens, and its ability to classify and identify the damage modes is investigated. The trained CNN achieves satisfactory classification accuracy of 96.9% on pristine quasi-isotropic specimen data and 96.4% on notched quasi-isotropic specimen data. When compared to the prediction accuracy of pure damage modes, the prediction accuracy of mixed-mode damage is slightly lower, at 92.5% for pristine and 91.3% for notched quasi-isotropic specimens. This reduction in accuracy is due to the spectrograms of mixed-mode damage containing energy distributed across multiple frequency bands. By classifying the AE waveforms of both pristine and notched quasi-isotropic specimens, the progression of different damage modes is analyzed through the cumulative count of AE waveforms associated with each damage type. These findings enhance the understanding of damage mod

关键词： Acoustic emission CFRP laminate convolutional autoencoder convolutional neural network damage classification deep learning algorithms

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A novel framework for generating handwritten datasets

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第6期80卷 9657-9669页

作者： Anwar, Sajid Mehrban, Bilal Ali, Musawar Hussain, Farhan Halim, Zahid Ghulam Ishaq Khan Inst Engn Sci & Technol Swabi Pakistan Univ AJK Muzaffarabad Pakistan Natl Univ Sci & Technol Islamabad Pakistan

The performance of deep learning algorithms is highly dependent on the size and diversity of data. However, for handwritten character recognition, dataset creation, segmentation, and labeling are time consuming and laborious tasks and not much researched. This work proposes a novel and generic framework which automates the segmentation and labeling processes for handwritten datasets. First, a user collects handwritten glyphs on the proposed form. Next, based on a priori knowledge, local peaks from horizontal and vertical projection functions are computed. This helps in locating and segmenting individual samples automatically. To show the effectiveness of the proposed framework, a dataset of 160,000 samples is collected for an oriental language. We profile the segmentation of samples from one sheet with three approaches: manual, semi-automatic, and the proposed fully automatic approach. Compared to the manual and semi-automatic processes, the proposed approach is 120 x and 65 x faster, respectively. Further, we also present the classification of this dataset by traditional and state-of-the-art machine learning algorithms.

关键词： Automatic segmentation Handwritten character recognition deep learning algorithms

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Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

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IEEE ACCESS 2022年 10卷 56863-56875页

作者： Pamir Javaid, Nadeem Qasim, Umar Yahaya, Adamu Sani Alkhammash, Eman H. Hadjouni, Myriam COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia Univ Engn & Technol & Lahore Dept Comp Sci New Campus Lahore 54000 Pakistan Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

关键词： Data models Support vector machines Logic gates Hardware Feature extraction Convolutional neural networks Meters Autoencoder bidirectional gated recurrent unit deep learning algorithms smart grid electricity theft detection machine learning algorithms synthetic theft attacks

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Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process

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APPLIED WATER SCIENCE 2023年第1期13卷 1-32页

作者： Chong, K. L. Huang, Y. F. Koo, C. H. Sherif, Mohsen Ahmed, Ali Najah El-Shafie, Ahmed Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Jalan Sg Long Kajang 43000 Selangor Malaysia United Arab Emirates Univ Coll Engn Civil & Environm Engn Dept Al Ain 15551 U Arab Emirates United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates Univ Tenaga Nasl UNITEN Coll Engn Inst Energy Infrastruct IEI Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia

Streamflow forecasting has always been important in water resources management, particularly the peak flow, which often determines the seriousness of the impending flood. However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. Due to the distinctive characteristics of these two adopted forms, selecting the correct algorithm for the machine learning problem along with their hyperparameter tuning process is critical to the realization of the desired results. For the distinct streamflow formulated scenarios, three neural network algorithms and their hyperparameter tuning strategy were investigated. The comparative empirical studies had revealed that formulated categorical-based streamflow forecast is a better choice than a regression-based streamflow forecast, regardless of the algorithms used;for instance, the f1-score of 0.7 (categorical based) is obtained compared to the 0.53 (regression based) for the LSTM in scenario 1 (binary). Furthermore, forest-based algorithms were investigated and shown to be superior at forecasting high streamflow fluctuations in situations featuring low-dimensional streamflow input. Besides, encoding the streamflow time series as images (input) for forecasting purposes would require a thorough analysis as there is a discrepancy in the results, revealing that not all approaches are suitable for streamflow image transformation. The functional ANOVA analysis provided evidence to substantiate the Bayesian optimization results, implying that the hyperparameters were effectively optimized.

关键词： Streamflow forecasting Hyperparameter optimization deep learning algorithms Machine learning algorithms

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Two-order graph convolutional networks for semi-supervised classification

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IET IMAGE PROCESSING 2019年第14期13卷 2763-2771页

作者： Fu Sichao Liu Weifeng Li Shuying Zhou Yicong China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Shandong Peoples R China Xian Univ Posts & Telecommun Sch Automat Xian 710121 Shaanxi Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Currently, deep learning (DL) algorithms have achieved great success in many applications including computer vision and natural language processing. Many different kinds of DL models have been reported, such as deepWalk, LINE, diffusionconvolutional neural networks, graph convolutional networks (GCN), and so on. The GCN algorithm is a variant of convolutional neural network and achieves significant superiority by using a one-order localised spectral graph filter. However, only a one-order polynomial in the Laplacian of GCN has been approximated and implemented, which ignores undirect neighbour structure information. The lack of rich structure information reduces the performance of the neural networks in the graph structure data. In this study, the authors deduce and simplify the formula of two-order spectral graph convolutions to preserve rich local information. Furthermore, they build a layerwise GCN based on this two-order approximation, i.e. two-order GCN (TGCN) for semi-supervised classification. With the two-order polynomial in the Laplacian, the proposed TGCN model can assimilate abundant localised structure information of graph data and then boosts the classification significantly. To evaluate the proposed solution, extensive experiments are conducted on several popular datasets including the Citeseer, Cora, and PubMed dataset. Experimental results demonstrate that the proposed TGCN outperforms the state-of-art methods.

关键词： approximation theory learning (artificial intelligence) pattern classification graph theory convolutional neural nets semisupervised classification deep learning algorithms natural language processing diffusion-convolutional neural networks GCN algorithm one-order localised spectral graph filter one-order polynomial Laplacian undirect neighbour structure information graph structure data two-order spectral graph convolutions two-order approximation two-order polynomial abundant localised structure information graph data computer vision two-order GCN layerwise GCN two-order graph convolutional networks semi-supervised classification

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Human-like evaluation method for object motion detection algorithms

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IET COMPUTER VISION 2020年第8期14卷 674-682页

作者： Guzman-Pando, Abimael Ignacio Chacon-Murguia, Mario Chacon-Diaz, Lucia B. Tecnol Nacl Mexico Visual Percept Lab Grad & Res Dept IT Chihuahua Ave Tecnol 2909 Chihuahua Mexico Ohio State Univ Coll Educ & Human Ecol Dept Teaching & Learning 1945 North High St Columbus OH 43210 USA

This study proposes a new method to evaluate the performance of algorithms for moving object detection (MODA) in video sequences. The proposed method is based on human performance metric intervals, instead of ideal metric values (0 or 1) which are commonly used in the literature. These intervals are proposed to establish a more reliable evaluation and comparison, and to identify areas of improvement in the evaluation of MODA. The contribution of the study includes the determination of human segmentation performance metric intervals and their comparison with state-of-the-art MODA, and the evaluation of their segmentation results in a tracking task to establish the impact between performance and practical utility. Results show that human participants had difficulty with achieving a perfect segmentation score. deep learning algorithms achieved performance above the human average, while other techniques achieved a performance between 88 and 92%. Furthermore, the authors demonstrate that algorithms not ranked at the top of the quantitative metrics worked satisfactorily in a tracking experiment;and therefore, should not be discarded for real applications.

关键词： image segmentation learning (artificial intelligence) object detection image motion analysis video signal processing image sequences evaluation method object motion detection algorithms object detection video sequences human performance metric intervals ideal metric values reliable evaluation human segmentation performance metric intervals state‐of‐the‐art MODA human participants perfect segmentation score deep learning algorithms human average quantitative metrics

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Classification of neurofibromatosis-related dystrophic or nondystrophic scoliosis based on image features using Bilateral CNN

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MEDICAL PHYSICS 2021年第4期48卷 1571-1583页

作者： He, Zhong Wang, Yimu Qin, Xiaodong Yin, Rui Qiu, Yong He, Kelei Zhu, Zezhang Nanjing Univ Dept Spine Surg Affiliated Drum Tower Hosp Med Sch Nanjing Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing Peoples R China Nanjing Univ Med Sch Nanjing Peoples R China Nanjing Univ Natl Inst Healthcare Data Sci Nanjing Peoples R China

Purpose We developed a system that can automatically classify cases of scoliosis secondary to neurofibromatosis type 1 (NF1-S) using deep learning algorithms (DLAs) and improve the accuracy and effectiveness of classification, thereby assisting surgeons with the auxiliary diagnosis. Methods Comprehensive experiments in NF1 classification were performed based on a dataset consisting 211 NF1-S (131 dystrophic and 80 nondystrophic NF1-S) patients. Additionally, 100 congenital scoliosis (CS), 100 adolescent idiopathic scoliosis (AIS) patients, and 114 normal controls were used for experiments in primary classification. For identification of NF1-S with nondystrophic or dystrophic curves, we devised a novel network (i.e., Bilateral convolutional neural network [CNN]) utilizing a bilinear-like operation to discover the similar interest features between whole spine AP and lateral x-ray images. The performance of Bilateral CNN was compared with spine surgeons, conventional DLAs (i.e., VGG-16, ResNet-50, and Bilinear CNN [BCNN]), recently proposed DLAs (i.e., ShuffleNet, MobileNet, and EfficientNet), and Two-path BCNN which was the extension of BCNN using AP and lateral x-ray images as inputs. Results In NF1 classification, our proposed Bilateral CNN with 80.36% accuracy outperformed the other seven DLAs ranging from 61.90% to 76.19% with fivefold cross-validation. It also outperformed the spine surgeons (with an average accuracy of 77.5% for the senior surgeons and 65.0% for the junior surgeons). Our method is highly generalizable due to the proposed methodology and data augmentation. Furthermore, the heatmaps extracted by Bilateral CNN showed curve pattern and morphology of ribs and vertebrae contributing most to the classification results. In primary classification, our proposed method with an accuracy of 87.92% also outperformed all the other methods with varied accuracies between 52.58% and 83.35% with fivefold cross-validation. Conclusions The proposed Bilateral CNN can

关键词： Bilateral CNN deep learning algorithms neurofibromatosis type 1 scoliosis classification

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A Novel deep Link Prediction Model for Peer-to-Peer Dynamic Task Collaboration Networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第4期15卷 1775-1791页

作者： Wu, Danfeng Zhang, Jiancheng Zhang, Jinlai Shi, Heng Beijing Union Univ Beijing Key Lab Informat Serv Engn 97 Beisihuan East Rd Beijing 100101 Peoples R China Beijing Union Univ Coll Robot 4 Gongti North Rd Beijing 100027 Peoples R China Beijing Union Univ Beijing Engn Res Ctr Smart Mech Innovat Design Se 4 Gongti North Rd Beijing 100027 Peoples R China Guangxi Univ Coll Mech Engn 100 Univ East Rd Nanning 530004 Guangxi Peoples R China Tsinhgua Univ Dept Precis Instruments 1 Qinghuayuan Beijing 100084 Peoples R China

In the dynamic and open peer-to-peer task collaboration scenarios, such as collaborative operations or post-earthquake coordinated rescue scenarios, the performance of personnel nodes or machine nodes will decrease with the consumption of energy, and the types of tasks that the nodes can perform change dynamically. Therefore, each node needs to dynamically maintain its immediate neighbors to guarantee the performance of task collaboration. In view of this, this paper pioneers the problem of directed link prediction for peer-to-peer dynamic task collaboration networks. First of all, the paper proposes two new link prediction metrics based on the link state change history, change time and multiple types of directed relationships between nodes. Secondly, based on the current feature vector and sequence feature vectors of related metrics between nodes, this paper reasonably designs the use mechanism of hybrid deep learning algorithms, and proposes a novel deep link prediction model. A large number of experiments have shown that the link prediction metrics we proposed are more suitable for the evolution of collaboration links under the dynamic peer-to-peer task collaboration environment, and the CFSF model achieves better prediction performance than other models.

关键词： Peer-to-peer task collaboration networks Directed link prediction Link prediction metrics deep learning algorithms

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Biosignals Monitoring for Driver Drowsiness Detection Using deep Neural Networks

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IEEE ACCESS 2024年 12卷 93075-93086页

作者： Alguindigue, Jose Singh, Amandeep Narayan, Apurva Samuel, Siby Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Western Univ Dept Comp Sci London ON N6A 3K7 Canada

Drowsy driving poses a significant risk to road safety, necessitating the development of reliable drowsiness detection systems. In particular, the advancement of Artificial Intelligence based neuroadaptive systems is imperative to effectively mitigate this risk. Towards reaching this goal, the present research focuses on investigating the efficacy of physiological indicators, including heart rate variability (HRV), percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals, in predicting driver drowsiness. The study was conducted with a cohort of 30 participants in controlled simulated driving scenarios, with half driving in a non-monotonous environment and the other half in a monotonous environment. Three deep learning algorithms were employed: sequential neural network (SNN) for HRV, 1D-convolutional neural network (1D-CNN) for EDA, and convolutional recurrent neural network (CRNN) for eye tracking. The HRV-Based Model and EDA-Based Model exhibited strong performance in drowsiness classification, with the HRV model achieving precision, recall, and F1-score of 98.28%, 98%, and 98%, respectively, and the EDA model achieving 96.32%, 96%, and 96% for the same metrics. The confusion matrix further illustrates the model's performance and highlights high accuracy in both HRV and EDA models, affirming their efficiency in detecting driver drowsiness. However, the Eye-Based Model faced difficulties in identifying drowsiness instances, potentially attributable to dataset imbalances and underrepresentation of specific fatigue states. Despite the challenges, this work significantly contributes to ongoing efforts to improve road safety by laying the foundation for effective real-time neuro-adaptive systems for drowsiness detection and mitigation.

关键词： Fatigue Heart rate variability Sleep Accuracy Measurement Feature extraction Road safety Simulation Gaze tracking deep learning Vehicle driving Vehicle safety drowsiness simulated driving heart rate variability electrodermal activity eye tracking deep learning algorithms

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