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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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High‑Energy-Density Fiber Supercapacitors Based on Transition Metal Oxide Nanoribbon Yarns for Comprehensive Wearable Electronics

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advanced Fiber Materials 2024年第6期6卷 1927-1941页

作者： Junseong Ahn Suchithra Padmajan Sasikala Yongrok Jeong Jin Goo Kim Ji‑Hwan Ha Soon Hyoung Hwang Sohee Jeon Junhyuk Choi Byung‑Ho Kang Jihyeon Ahn Jun‑Ho Jeong Sang Ouk Kim Inkyu Park Department of Electro‑Mechanical Systems Engineering Korea UniversitySejong 30019Republic of Korea Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea National Creative Research Initiative Center for Multi‑Dimensional Directed Nanoscale Assembly Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Department of Nano Manufacturing Technology Korea Institute of Machinery and Materials(KIMM)Daejeon 34103Republic of Korea Radioisotope Research Division Korea Atomic Energy Research Institute(KAERI)Daejeon 34057Republic of Korea

Fiber supercapacitors(FSs)based on transition metal oxides(TMOs)have garnered considerable attention as energy stor-age solutions for wearable electronics owing to their exceptional characteristics,including superior comfortability and low *** materials are known to exhibit high energy densities,high specific capacitances,and fast redox ***,current fabrication methods for these structures primarily rely on chemical deposition,often resulting in undesir-able material structures and necessitating the use of additives,which can degrade the electrochemical performance of such ***,physically deposited TMO nanoribbon yarns generated via delamination engineering of nanopatterned TMO/metal/TMO trilayer arrays are proposed as potential high-performance *** prepare these arrays,the target materials were initially deposited using a nanoline mold,and subsequently,the nanoribbon was suspended through selective plasma etching to obtain the desired twisted yarn *** of the direct formation of TMOs on Ni electrodes,a high energy/power density and excellent electrochemical stability were achieved in asymmetric FS devices incorporating CoNixOy nanoribbon yarns and graphene ***,a triboelectric nanogenerator,pressure sensor,and flexible light-emitting diode were synergistically combined with the *** integration of wearable electronic components,encompassing energy harvesting,energy storage,and powering sensing/display devices,is promising for the development of future smart textiles.

关键词： Fiber supercapacitor Transition metal oxide Nanoribbon yarn Wearable devices Nanostructuring

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A disposable tartrazine sensor fabricated with a synchronously activated nanocomposite comprising gadolinium molybdate nanoflowers anchored on functionalized carbon nanofibers

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New Journal of Chemistry 2023年第6期48卷 2660-2672页

作者： Jayapaul, Abishek Sakthivel, Rajalakshmi Lin, Yu-Chien Dhawan, Udesh Liu, Xinke Wen, Hsiao-Wei Chung, Ren-Jei No. 1 Section 3 Zhongxiao E. Rd. Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Food Science and Biotechnology National Chung Hsing University Taichung402202 Taiwan Food and Animal Product Safety Inspection Center National Chung Hsing University Taichung402202 Taiwan Taipei10608 Taiwan

Tartrazine (TRZ) is a synthetic food dye considered noxious to human health and a toxic environmental contaminant owing to the presence of a harmful azo group (-N 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 N-) in its chemical structure. The global necessity to prevent food contamination has led scientific researchers to develop disposable sensing platforms. This study aims to integrate nanotechnology with electrochemistry to fabricate a disposable screen-printed carbon electrode (SPCE) with a nanocomposite composed of gadolinium molybdate (Gd2MoO6) nanoflowers decorated on an enmeshed matrix of acid-functionalized carbon nanofibers (f-CNF). The hierarchal arrangement of distinct components in the nanocomposite is promoted by synergism through the formation of a hybrid heterojunction. This heterojunction in the electrode modifier activates the electrode to sense TRZ with a remarkable limit of detection (0.8 nmol L−1) and quantification (2 nmol L−1) over a broad linear range of 0.025 μM to 216.45 μM. Furthermore, the functional sensing ability of the proposed sensor was analyzed in various commercial food products. © 2024 The Royal Society of Chemistry.

关键词： Nanocomposites

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Rock mass joint set identification at Draa Sfar mine in Morocco through stereographic projection and K-means clustering

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Mediterranean Geoscience Reviews 2024年第1期6卷 49-56页

作者： Clero, Kaoutar Ed-Diny, Said Achalhi, Mohammed Cherkaoui, Mouhamed Benzakour, Intissar Soror, Tarik Rziki, Said El Fkihi, Sanaa Nadour, Mohamed Tagemouati, Hicham Bourzeix, François National School of Mines of Rabat (ENSMR) Hadj Ahmed Cherkaoui Avenue Agdal Rabat Morocco Moroccan Foundation for Advanced Science Innovation and Research (MASCIR) Mohamed Al Jazouli Street Madinat Al Irfane Rabat Morocco REMINEX Engineering R&D and Project Management BP 469 Marrakech Morocco National School of Computer Science and Systems Analysis (ENSIAS) IRDA Team Rabat IT Center ADMIR Lab Université Mohammed V Rabat Morocco

Rock mass joint set identification, crucial for geological and geotechnical studies, is often based on stereographic projection. However, the related software programs can eventually present some access limitations. Therefore, with the development of the Artificial Intelligence field and the sharp increase in Machine Learning and Deep Learning applications, this study aims to suggest an alternative solution for rock mass joint sets identification based on the K-means clustering algorithm, an unsupervised Machine Learning model that aims to structure unlabeled data into different clusters based on their patterns. The study includes a comparative analysis of joint set clustering using the K-means model and the stereographic projection. The two methods are applied to the structural data of the Draa Sfar deep underground mine in Morocco. The results show the concordance of the joint set clustering using both stereographic projection and the K-means model, highlighted by the mean dip and dip direction obtained values. This concordance confirms that K-means clustering allows identifying rock mass joint sets if access to stereographic projection automated tools is limited. However, using K-means clustering for this purpose requires applying preprocessing methods such as the elbow method or the silhouette score to identify the clusters' number previously. In conclusion, unsupervised learning can be an alternative for identifying rock mass joint sets as long as enough and suitable structural data are accessible. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Artificial intelligence K-means clustering Rock mass joint set Stereographic projection Structural geology Unsupervised learning

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Characterization of Systematic Bias in ALOS-2 Multilooked Interferograms

Characterization of Systematic Bias in ALOS-2 Multilooked In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ryu Sugimoto Yu Morishita Masanobu Shimada Ryo Natsuaki Chiaki Tsutsumi Ryosuke Nakamura Toru Kouyama Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Japan Department of Electrical Engineering and Information Systems The University of Tokyo Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Interferometric SAR time series analysis using multilooked interferograms has measured ground deformation with high accuracy over large areas, but is known to be contaminated by a systematic bias when using only short-term interferograms. This bias, also referred to as a "fading signal", has been investigated using the abundant time series data from Sentinel-1 C-band SAR, and soil moisture and biomass changes have been suggested as two possible causes for the observed bias. Several correction methods have been proposed to mitigate the phase bias, assuming a periodic observation strategy. The phase bias is supposed to increase with the wavelength, but the bias in ALOS-2 L-band SAR has not been investigated in detail because the data distribution policy restricts its users. In addition, these correction methods are not suitable for the infrequently observed data such as ALOS-2. In this paper, we investigated the systematic bias in ALOS-2 multilooked interferograms with respect to various interferometric pairs. We also corrected the phase bias using the noise-filtering technique. The bias in ALOS-2 multilooked interferograms showed >3 mm/year with an average temporal baseline of 144 days, which is equivalent to that of Sentinel-1 with the temporal baseline of 78 days. That is, the bias effect toward the temporal baseline was larger in ALOS-2 than in Sentinel-1. The noise-filtering technique could mitigate the bias with less pairs regardless of land-use.

关键词： Fading channels Systematics Soil measurements Time series analysis Soil moisture Sentinel-1 Geoscience and remote sensing

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Distributed Active Disturbance Rejection Control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

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2023 China Automation Congress, CAC 2023

作者： Li, Xin Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350303759

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method. © 2023 IEEE.

关键词： Controllers

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