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deep learning based method for false data injection attack detection in AC smart islands

IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5756-5765页

作者： Dehghani, Moslem Kavousi-Fard, Abdollah Dabbaghjamanesh, Morteza Avatefipour, Omid Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Texas Dallas Dept Elect & Elect Engn Richardson TX 75083 USA Univ Michigan Dept Elect & Elect Engn Dearborn MI 48128 USA

This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island. In this study, a new scheme of FDIA detection is proposed based on wavelet singular values as input index of deep learning algorithm. In the proposed method, switching surface based on sliding mode control breaks down for adjusting accurate factors of wavelet transform and then features of wavelet coefficients are extracted by singular value decomposition. Indexes are determined according to the wavelet singular values in switching surface of voltage and current which defines the input indexes of deep machine learning and detecting FDIA. This cyber-protection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending and receiving data from communication and control systems. The main priority of the suggested detection plan is the high capability to detect FDIA with a high accuracy. To show the effectiveness of the proposed method, simulation studies are performed on AC smart island in MATLAB/Simulink environment.

关键词： wavelet transforms power distribution faults security of data variable structure systems learning (artificial intelligence) smart power grids singular value decomposition distributed power generation power engineering computing power system security fault diagnosis suggested detection plan deep machine learning singular value decomposition wavelet coefficients deep learning algorithm input index wavelet singular values FDIA detection distributed energy resources detection solution false data injection attacks AC smart island false data injection attack detection

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deep neural network based m-learning model for predicting mobile learners' performance

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第3期28卷 1422-1441页

作者： Adnan, Muhammad Habib, Asad Ashraf, Jawad Mussadiq, Shafaq Raza, Arsalan Ali Kohat Univ Sci & Technol Fac Numer & Phys Sci Inst Comp Kohat Pakistan COMSATS Univ Islamabad Fac Sci Dept Comp Sci Vehari Campus Vehari Punjab Pakistan

The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners' performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

关键词： M-learning model artificial neural network deep learning algorithm features importance machine learning mobile learning

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Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis

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JOURNAL OF STOMATOLOGY ORAL AND MAXILLOFACIAL SURGERY 2024年第3期125卷 101840页

作者： Li, Chenxi Chen, Xinya Chen, Cheng Gong, Zhongcheng Pataer, Parekejiang Liu, Xu Lv, Xiaoyi Xinjiang Med Univ Oncol Dept Oral & Maxillofacial Surg Affiliated Hosp 1 Sch Hosp Stomatol Urumqi 830054 Peoples R China Stomatol Res Inst Xinjiang Uygur Autonomous Reg 137 Liyushan South Rd Urumqi 830054 Peoples R China Huazhong Univ Sci & Technol Sch Stomatol Union Hosp Tongji Med CollHubei Prov Key Lab Oral & Maxillo Wuhan 430022 Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi 830008 Peoples R China Xinjiang Univ Coll Software Urumqi 830046 Peoples R China Lanzhou Univ Dept Maxillofacial Surg Key Lab Dent Maxillofacial Reconstruct & Biol Int Fac DentHosp Stomatol Lanzhou 730013 Peoples R China

Objective: To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC). Materials and methods: Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation;random-effects model was used;and results were reported according to the PRISMA statement. Results: A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87 similar to 0.88) and 0.80 (95 %CI: 0.80 similar to 0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03). Conclusions: The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article. (c) 2024 Elsevier Masson SAS. All rights reserved.

关键词： Radiomics Precise medicine Oscc treatment Medical imaging technology deep learning algorithm

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THERMAL STATION MODELLING AND OPTIMAL CONTROL BASED ON deep learning

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THERMAL SCIENCE 2021年第4期25卷 2965-2973页

作者： Cao, Min Zhengzhou Univ Technol Zhengzhou Peoples R China

To solve the mismatch between heating quantity and demand of thermal stations, an optimized control method based on depth deterministic strategy gradient was proposed in this paper. In this paper, long short-time memory deep learning algorithm is used to model the thermal power station, and then the depth deterministic strategy gradient control algorithm is used to solve the water supply flow sequence of the primary side of the thermal power station in combination with the operation mechanism of the central heating system. In this paper, a large number of historical working condition data of a thermal station are used to carry out simulation experiment, and the results show that the method is effective, which can realize the on-demand heating of the thermal station a certain extent and improve the utilization rate of heat.

关键词： deterministic strategy short- and long-term memory networks gradient thermal power station optimization control deep learning algorithm

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Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and deep learning

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ACS NANO 2023年第1期17卷 539-551页

作者： Lee, Kichul Cho, Incheol Kang, Mingu Jeong, Jaeseok Choi, Minho Woo, Kie Young Yoon, Kuk-Jin Cho, Yong-Hoon Park, Inkyu Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KAIST Dept Phys Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KAIST KAIST Inst NanoCentury Daejeon 34141 South Korea

As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (mu LED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In2O3 film coated with plasmonic metal nanoparticles (NPs) directly on the mu LED. The e-nose system consists of two different mu LED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-mu LED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO2, acetone, methanol) was developed. The mu LED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.

关键词： micro-LED monolithic photoactivated gas sensor ultra-low-power localized surface plasmon resonance deep learning algorithm electronic nose

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EMI Radiation Prediction and Structure Optimization of Packages by deep learning

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IEEE ACCESS 2019年 7卷 93772-93780页

作者： Jin, Hang Ma, Hanzhi Butala, Mark D. Liu, En-Xiao Li, Er-Ping Zhejiang Univ Zhejiang Univ Univ Illinois Urbana Champaign Inst Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore 138632 Singapore

With a rapid increase in operating frequency and package complexity, conventional analysis methods cannot efficiently cope with current complex electromagnetic interference (EMI) issues. In this paper, a new method built on a deep neural network (DNN) model is proposed to accurately and rapidly predict the maximum radiated electric field at 3-meters of a wire-bond ball grid array (WB-BGA) package. The key hyper-parameters of the DNN model, such as the learning rate and type of optimizers, are discussed in depth so as to attain optimal performance. Predicted radiation results by the DNN model and the results of the full-wave simulation show good agreement. Once DNN is trained, the prediction time is in the order of milliseconds and the model size is in megabytes, which can acquire the predicted radiation quickly and accurately and save storage space. Furthermore, to prevent the radiation from exceeding requirements, package structures are optimized by adjusting those parameters sensitive to radiation and disregarding insensitive parameters. The sensitivity of these WB-BGA package structural parameters to EMI radiation can be analyzed quickly based on data with different deviations generated by the trained DNN model. The sensitive parameters are adjusted according to their correlation with EMI radiation. The effectiveness and feasibility of the optimization method are verified by the WB-BGA package with two sets of different structural parameters.

关键词： deep learning algorithm deep neural network electromagnetic interference radiation prediction sensitivity analysis structure optimization WB-BGA package

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Rapid profiling of carcinogenic types of Helicobacter pylori infection via deep learning analysis of label-free SERS spectra of human serum

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2024年 23卷 3379-3390页

作者： Li, Fen Si, Yu-Ting Tang, Jia-Wei Umar, Zeeshan Xiong, Xue-Song Wang, Jin-Ting Yuan, Quan Tay, Alfred Chin Yen Chua, Eng Guan Zhang, Li Marshall, Barry J. Yang, Wei-Xuan Gu, Bing Wang, Liang Yangzhou Univ Huaian Hosp Peoples Hosp Huaian 5 Dept Lab Med Huaian Jiangsu Peoples R China Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Lab Med Guangzhou Guangdong Peoples R China Xuzhou Med Univ Med Technol Sch Xuzhou Jiangsu Peoples R China Shenzhen Univ Sch Biomed Engn Int Canc Ctr Med SchMarshall Lab Biomed Engn Shenzhen Guangdong Peoples R China Yangzhou Univ Huaian Hosp Peoples Hosp Huaian 5 Dept Gastroenterol Huaian Jiangsu Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Dept Intelligent Med Engn Xuzhou Jiangsu Peoples R China Univ Western Australia Marshall Ctr Infect Dis Res & Training Perth WA Australia Zhengzhou Univ Affiliated Hosp 5 Marshall Med Res Ctr Zhengzhou Henan Peoples R China Zhengzhou Univ Marshall Int Digest Dis Hosp Zhengzhou Henan Peoples R China Univ New South Wales Sch Biotechnol & Biomol Sci Sydney NSW 2052 Australia Univ Western Australia Sch Biomed Sci Div Microbiol & Immunol Crawley WA Australia Edith Cowan Univ Sch Med & Hlth Sci Ctr Precis Hlth Perth WA Australia Univ Queensland Sch Agr & Food Sustainabil Brisbane Qld Australia

WHO classified Helicobacter pylori as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of H. pylori infection, only about 3 % of infected individuals eventually develop gastric cancer, with the highly virulent H. pylori strains expressing cytotoxin-associated protein (CagA) and vacuolating cytotoxin (VacA) being critical factors in gastric carcinogenesis. It is well known that H. pylori infection is divided into two types in terms of the presence and absence of CagA and VacA toxins in serum, that is, carcinogenic Type I infection (CagA+/VacA+, CagA+/VacA-, CagA-/VacA+) and non-carcinogenic Type II infection (CagA-/VacA-). Currently, detecting the two carcinogenic toxins in active modes is mainly done by diagnosing their serological antibodies. However, the method is restricted by expensive reagents and intricate procedures. Therefore, establishing a rapid, accurate, and cost-effective way for serological profiling of carcinogenic H. pylori infection holds significant implications for effectively guiding H. pylori eradication and gastric cancer prevention. In this study, we developed a novel method by combining surface-enhanced Raman spectroscopy with the deep learning algorithm convolutional neural network to create a model for distinguishing between serum samples with Type I and Type II H. pylori infections. This method holds the potential to facilitate rapid screening of H. pylori infections with high risks of carcinogenesis at the population level, which can have long-term benefits in reducing gastric cancer incidence when used for guiding the eradication of H. pylori infections.

关键词： Helicobacter pylori Surface-enhanced Raman spectrometry deep learning algorithm Gastric cancer Serum antibody Carcinogenic toxin

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Structural reliability analysis using temporal deep learning-based model and importance sampling

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STRUCTURAL ENGINEERING AND MECHANICS 2022年第3期84卷 323-335页

作者： Nguyen, Truong-Thang Dang, Viet-Hung Hanoi Univ Civil Engn Fac Bldg & Ind Construct Hanoi Vietnam

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high -dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting -edge deep learning architectures specialized for capturing temporal relationships within time -series data, namely Long -Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

关键词： deep learning algorithm numerical simulation reliability analysis stochastic processes structural engineering

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A Framework for RQD Calculation Based on deep learning

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MINING METALLURGY & EXPLORATION 2023年第5期40卷 1567-1583页

作者： Su, Rui Zhao, Qihua Zheng, Tao Han, Gang Jiang, Junfeng Hu, Jianbo Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China 106 Geol Brigade Sichuan Bur Geol & Mineral Resour Chengdu 611130 Peoples R China

The rock quality designation (RQD) is a commonly used index for the classification and evaluation of rock mass quality and is widely adopted in mining and geological engineering. The traditional method of obtaining RQD still requires manual measurement of core length and calculation of RQD, which is inefficient. To address this problem, we propose a framework for drilling core image segmentation and RQD estimation from digital images of cores in core boxes based on the deep learning algorithm. The proposed framework is generated by combining the Mask Region-Based Convolutional Neural Networks (Mask R-CNN) instance segmentation algorithm, the U-Shaped Convolutional Neural Networks (U-Net) semantic segmentation algorithm, and the image processing functions of the Open Source Computer Vision Library (OpenCV). To demonstrate the accuracy of the proposed method, seven boreholes in the Xiushuihe vanadium-titanium magnetite mine, located in the Sichuan Province, China, were used as the case study. According to the comparison of the manual measurements and calculations of the cores taken from seven boreholes used to conduct this study, the framework can record the length of the drill cores and calculate RQD within an average error rate of 3.42%, while it saves about 85% of the working time. The results illustrate that the proposed framework enhances the efficiency of RQD calculation while satisfying the engineering requirements.

关键词： RQD deep learning algorithm Image segmentation OpenCV

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Analysis of multi-level capital market linkage driven by artificial intelligence and deep learning methods

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SOFT COMPUTING 2020年第11期24卷 8011-8019页

作者： Jing, Xinxin Peng, Pin Huang, Zhe Zhongnan Univ Econ & Law Wuhan 430073 Peoples R China Shanghai Univ Finance & Econ Jinhua 321000 Zhejiang Peoples R China

Artificial intelligence has a very wide application in modern society. deep learning algorithms promote the development of artificial intelligence industry. The development of artificial intelligence industry needs the support of capital market. As far as the relationship between the three is concerned, the artificial intelligence industry is in the middle position. In short, the computer deep learning algorithm is the technical basis for the development of artificial intelligence, and the capital market is an important guarantee for the development of the artificial intelligence industry. However, under the current development background of China, the capital market is far from meeting the improvement and development of high-tech industries. Therefore, in order to further clarify the important role of deep learning algorithms in the development of artificial intelligence and capital markets, this paper systematically analyzes these three relationships and proposes measures from the perspective of supporting theoretical research and development and improving capital markets.

关键词： deep learning algorithm Artificial intelligence Capital market

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