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Towards an Internet-like Power Grid

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 1-11页

作者： Iasonas Kouveliotis-Lysikatos Nikos Hatziargyriou Yanli Liu Felix Wu department of electrical engineering and computer Science KTH-Royal Institute of TechnologyTeknikringen 3310040StockholmSweden School of electrical and computer engineering National Technical University of Athens15773 AthensGreece School of electrical and Information engineering Tianjin UniversityTianjin300072China department of electrical and Electronic engineering The University of Hong KongHong KongChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA

The great challenges faced by modern power systems require a fresh look at the conventional operation paradigm. The significant challenges faced by modern power systems require an innovative method for the conventional operation paradigm. We claim that the decarbonization of the power grid and extensive electrification of numerous sectors of human activity can only be fostered by a self-adaptable and smart power grid that manifests similar qualities to those of the Internet. The Internet is constructed on a layered architecture that facilitates technology innovations and its intelligence is distributed throughout a hierarchy of networks. In this paper, the fundamental differences between the network data flows and power flows are examined, and the basic requirements for an innovative operation paradigm are highlighted. The current power grid is operated in a highly inflexible, centralized manner to meet increased security goals. A new highly flexible, distributed architecture can be realized by distributing the operation responsibility in smaller areas or even in grid components that can make autonomous decisions. The characteristics of such a power grid are presented, and the key features and advances for the on-going transition to a sustainable power system are identified. Finally, a case study on distributed voltage control is presented and discussed.

关键词： Smart grid Internet distributed intelligence decentralized architecture

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High voltage outdoor insulator surface condition evaluation using aerial insulator images

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High Voltage 2019年第3期4卷 178-185页

作者： Damira Pernebayeva Aidana Irmanova Diana Sadykova Mehdi Bagheri Alex James department of electrical and computer engineering School of Engineering and Digital Sciences Nazarbayev UniversityAstanaKazakhstan

High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains.The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods.Two different types of classification approaches are compared:one method is based on neural networks(e.g.CNN,InceptionV3,MobileNet,VGG16,and ResNet50)and the other method is based on traditional machine learning classifiers(e.g.Bayes Net,Decision Tree,Lazy,Rules,and Meta classifiers).They are evaluated to discriminate the images of insulator surface exposed to freezing,wet,and snowing conditions.The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy.The classification of the insulator surfaces will assist in determining the insulator conditions,and take preventive measures for its protection.

关键词： insulator surface condition

Security in edge-assisted Internet of Things:challenges and solutions

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Science China(Information sciences) 2020年第12期63卷 27-40页

作者： Shuaiqi SHEN Kuan ZHANG Yi ZHOU Song CI department of electrical and computer engineering University of Nebraska-Lincoln School of computer and Information engineering Henan University department of electrical engineering Tsinghua University

The flourish of 5th generation wireless systems(5G) network has brought numerous benefits to the Internet of Things(IoT) with universal connectivity, improved data rate, and decreased latency. The development of IoT extends the computation-intensive applications from centralized servers to the edge of the network, promoting the paradigm of edge computing. Edge computing brings great assistance to IoT architecture by efficiently accomplishing tasks with lower latency, less energy consumption and reduced network bandwidth. Edge-assisted IoT emerges to provide location-aware services and offload computational tasks to edge nodes near the IoT devices. However, a series of security challenges still exist in edge-assisted IoT owing to the inherent vulnerabilities of edge nodes and sensitive nature of the collected data. Among the emerging and existing security schemes in IoT applications, security and energy consumption are two overriding yet conflicting requirements. The tradeoff between security and energy is critical to the sustainability of the IoT ecosystem but still lacks sufficient discussion. In this article, we investigate the security challenges in edge-assisted IoT and how the constraints in energy consumption can affect the design of security schemes.Specifically, we firstly present the architecture of edge-assisted IoT and its unique characteristics. Secondly,we identify the security threats in the edge-assisted IoT applications and the security-energy tradeoff during implementation. Thirdly, in a case study of distributed denial of service(DDoS) and malware injection attack, we propose a preliminary solution to address the conflict between security and energy requirements.Finally, we discuss some open issues and identify future research directions for security and energy efficiency in edge-assisted IoT.

关键词： Internet of Things edge computing network security machine learning data analytics

Early-Stage Segmentation and Characterization of Brain Tumor

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computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman department of computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of computer Science and engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan department of electrical and computer engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues.The life expectancy of patients diagnosed with gliomas decreases exponentially.Most gliomas are diagnosed in later stages,resulting in imminent death.On average,patients do not survive 14 months after diagnosis.The only way to minimize the impact of this inevitable disease is through early diagnosis.The Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain tissues.The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors.Besides this,dealing with MRI scans manually is also cumbersome,thus affects the classification accuracy.To eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification task.Although,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing region.This is because of low variance in enhancing region which give poor contrast in MRI scans.In this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing region.Our trained model achieved better results for all glioma classes including the enhancing region.The improved performance of our technique can be attributed to several factors.Firstly,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant noise.Secondly,the architecture we employ can capture the non-linearity of all classes including the enhancing region.Overall,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

Parallel intelligent education with ChatGPT

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG department of computer Science and Software engineering Monmouth UniversityNew Jersey 07764USA department of electrical and computer engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning experience.We especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class learning.We address the strengths and weaknesses of learning with ChatGPT.Finally,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

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computers, Materials & Continua 2022年第2期70卷 3235-3250页

作者： Javaria Tahir Syed Rameez Naqvi Khursheed Aurangzeb Musaed Alhussein department of computer Science COMSATS University IslamabadIslamabad CampusPakistan department of electrical and computer engineering COMSATS University IslamabadWah CampusPakistan department of computer engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Melanoma,due to its higher mortality rate,is considered as one of the most pernicious types of skin cancers,mostly affecting the white populations.It has been reported a number of times and is now widely accepted,that early detection of melanoma increases the chances of the subject’s survival.computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques.In thiswork,we propose a framework that accurately segments,and later classifies,the lesion using improved image segmentation and fusion methods.The proposed technique takes an image and passes it through two methods simultaneously;one is the weighted visual saliency-based method,and the second is improved HDCT based saliency estimation.The resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion region.The resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model-trained by applying transfer learning.The simulation results show improved performance compared to several existing methods.

关键词： Skin lesion segmentation image fusion saliency detection skin lesion classification deep neural networks transfer learning

Neural connectivity inference with spike-timing dependent plasticity network

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Science China(Information sciences) 2021年第6期64卷 70-79页

作者： John MOON Yuting WU Xiaojian ZHU Wei D.LU department of electrical engineering and computer Science University of Michigan

Knowing the connectivity patterns in neural circuitry is essential to understand the operating mechanism of the brain, as it allows the analysis of how neural signals are processed and flown through the neural system. With the recent advances in neural recording technologies in terms of channel size and time resolution, a simple and efficient system to perform neural connectivity inference is highly desired, which will enable the process of high dimensional neural activity recording data and reduction of the computational time and cost. In this work, we show that the spike-timing dependent plasticity(STDP) algorithm can be used to reconstruct neural connectivity patterns in a biological neural network, with higher accuracy and efficiency than statistic-based inference methods. The biologically inspired STDP learning rules are natively implemented in a second-order memristor network and are used to estimate the type and the direction of neural connections. When stimulated by the recorded neural spike trains, the memristor device conductance is modulated by the proposed STDP learning rules, which in turn reflects the correlation of the spikes and the possibility of neural connections. By compensating for the different levels of neural activity, highly reliable inference performance can be achieved. The proposed approach offers real-time and local learning, resulting in reduced computational cost/time and strong tolerance to variations of the neural system.

关键词： spike-timing dependent plasticity neural connectivity memristor online learning second-order memristor

Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets

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Artificial Intelligence in Agriculture 2022年第1期6卷 68-76页

作者： Sambandh Bhusan Dhal Muthukumar Bagavathiannan Ulisses Braga-Neto Stavros Kalafatis department of electrical and computer engineering Texas A&M UniversityUSA department of Soil and Crop sciences Texas A&M UniversityUSA

With the recent trends in urban agriculture and climate change,there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated.Hydroponic and aquaponic growth techniques have proven to be viable alternatives,but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis.The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs,for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale.One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches.In this paper,several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated,for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments.After repeated tests on the dataset,it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one.A set of recommended rules have been prescribed as a Decision Support System,using the output of the Machine Learning algorithm,which have been tested against the results of the baseline model.Further,the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.

关键词： Hydroponic Aquaponic Training datasets Non-linear algorithms Semi-bolstered error estimation Linear support vector machine Decision Support System

An end-to-end network for co-saliency detection in one single image

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Science China(Information sciences) 2023年第11期66卷 5-22页

作者： Yuanhao YUE Qin ZOU Hongkai YU Qian WANG Zhongyuan WANG Song WANG School of Cyber Science and engineering Wuhan University School of computer Science Wuhan University department of electrical engineering and computer Science Cleveland State University department of computer Science and engineering University of South Carolina

Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.

关键词： saliency detection convolutional neural network regional feature mapping co-saliency detection deep learning