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A novel MEMS inertial sensor with enhanced sensing capacitors

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Journal of Semiconductors 2009年第5期30卷 36-42页

作者：董林玺颜海霞霍卫红许立李永杰孙玲玲 Key Laboratory of RF Circuits and systems Ministry of EducationHangzhou Dianzi University Toshiba Hydro-Electro Equipments company

A novel MEMS inertial sensor with enhanced sensing capacitors is developed. The designed fabricated process of the sensor is a deep RIE process, which can increase the mass of the seismic to reduce the mechanical noise, and the designed capacitance sensing method is changing the capacitance area, which can reduce the air damping between the sensing capacitor plates and reduce the requirement for the DRIE process precision, and reduce the electronic noise by increasing the sensing voltage to improve the resolution. The design and simulation are also verified by using the FEM tool ANSYS. The simulated results show that the transverse sensitivity of the sensor is approximately equal to zero. Finally, the fabricated process based on silicon-glass bonding and the preliminary test results of the device for testing grid capacitors and the novel inertial sensor are presented. The testing quality factor of the testing device based on the slide-film damping effect is 514, which shows that the enhanced capacitors can reduce mechanical noise. The preliminary testing result of the sensitivity is 0.492pf/g.

关键词： capacitive accelerometer inertial sensor high precision DRIE

Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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computer Modeling in Engineering & Sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples

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Intelligent Automation & Soft Computing 2023年第2期35卷 1815-1829页

作者： Saud S.Alotaibi Department of Information systems College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

One of the most critical objectives of precision farming is to assess the germination quality of *** models contribute to thisﬁeld primarily through the use of artiﬁcial intelligence techniques such as machine learning,which present difﬁculties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another signiﬁcant dif-ﬁculty is assessing germination ***,the majority of these contri-butions make use of benchmark classiﬁcation methods that are either inept or too complex to train with the supplied *** manuscript addressed these issues by introducing a novel ensemble classiﬁcation strategy dubbed“Assessing Germination Quality of Seed Samples(AGQSS)by Adaptive Boosting Ensemble Classiﬁcation”that learns from quantitative phase features as well as universal features in greyscale spectroscopic *** experimental inquiry illustrates the signiﬁcance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.

关键词： Precision farming ensemble classiﬁcation germination quality machine learning predictive analytics

Hybrid immunizing solution for job recommender system

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Frontiers of computer Science 2017年第3期11卷 511-527页

作者： Shaha AL-OTAIBI Mourad YKHLEF Department of Information systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Information systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Two traditional recommendation techniques, content-based and collaborative filtering （CF）, have been widely used in a broad range of domain areas. Both meth- ods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommen- dation problem from applicant＇s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system （AIS）, which has a powerful explo- ration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors＇ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.

关键词： content-based collaborative filtering (CF) hy- bridization computational intelligence (CI) artificial im- mune system (AIS) clonal selection correlation-based simi- larity

Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

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computers, Materials & Continua 2021年第4期67卷 1013-1032页

作者： Hanan Abdullah Mengash Hanan A.Hosni Mahmoud Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of computer and systems Engineering Faculty of EngineeringUniversity of AlexandriaAlexandriaEgypt

:Strabismus is a medical condition that is defined as the lack of coordination between the *** Strabismus is detected at an early age,the chances of curing it are *** methods used to detect strabismus and measure its degree of deviation are complex and time-consuming,and they always require the presence of a *** this paper,we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover *** method involves extracting features from a set of training videos(training corpora)and using them to build a classifier.A decision tree(ID3)is built using labeled cases from actual strabismus *** are extracted from the corresponding videos of patients,and an association between the extracted features and actual diagnoses is *** Rules from the correlation plot are used to predict diagnoses for future *** classifier was tested using a set of testing videos(testing corpora).The results showed 95.9%accuracy,4.1%were light cases and could not be detected correctly from the videos,half of them were false positive and the other half was false negative.

关键词： Data mining strabismus video analysis angle of deviation decision tree

A novel robust proportional-integral (PI) adaptive observer design for chaos synchronization

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Chinese Physics B 2011年第12期20卷 116-122页

作者： Mahdi Pourgholi Vahid Johari Majd Intelligent Control systems Laboratory School of Electrical and Computer EngineeringTarbiat Modares UniversityTehranIran

In this paper, chaos synchronization in the presence of parameter uncertainty, observer gain perturbation and exogenous input disturbance is considered. A nonlinear non-fragile proportional-integral （PI） adaptive observer is designed for the synchronization of chaotic systems; its stability conditions based on the Lyapunov technique are derived. The observer proportional and integral gains, by converting the conditions into linear matrix inequality （LMI）, are optimally selected from solutions that satisfy the observer stability conditions such that the effect of disturbance on the synchronization error becomes minimized. To show the effectiveness of the proposed method, simulation results for the synchronization of a Lorenz chaotic system with unknown parameters in the presence of an exogenous input disturbance and abrupt gain perturbation are reported.

关键词： adaptive observer H-infinity design chaos synchronization linear matrix inequality

BSD/I18N—Internationalization of the 4.3BSD U N IX System

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Journal of computer Science & Technology 1993年第4期8卷 337-346页

作者：周建强杨立群潘世雷谈宏 Dept.of computer Science Nanjing UniversityNanjing 210008 Zhongli(Omron Shanghai)computer company Shanghai 200030 Dept.of computer Science Shanghai Jiaotong UniversityShanghai 200030

BSD/118N prelect is established for the purpose of internationalizing the 4.3BSD UNIX systems. Its tasks are to provide an internationalized application software development environment,support the 4.3BSD commands/utilities with multi-language processing,and build a complete English and Japanese us- er environments without excluding other languages like Chinese and Korean.

关键词： Intermationalization UNIX system character set environment

Structural properties and generative model of non-giant connected components in social networks

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Science China Earth Sciences 2016年第12期59卷 229-234页

作者： Jianwei NIU Lei WANG State Key Laboratory of Virtual Reality Technology and systems School of Computer Science and EngineeringBeihang University

Most previous studies have mainly focused on the analyses of one entire network(graph) or the giant connected components of networks. In this paper, we investigate the disconnected components(non-giant connected component) of some real social networks, and report some interesting discoveries about structural properties of disconnected components. We study three diverse, real networks and compute the significance profile of each component. We discover some similarities in the local structure between the giant connected component and disconnected components in diverse social networks. Then we discuss how to detect network attacks based on the local structure properties of networks. Furthermore, we propose an empirical generative model called i Friends to generate networks that follow our observed patterns.

关键词： disconnected components giant connected component structural properties significance profile generative model

Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

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computers, Materials & Continua 2021年第4期67卷 979-990页

作者： Nancy Awadallah Awad Department of computer and Information systems Sadat Academy for Management SciencesCairo11742Egypt

After the digital revolution,large quantities of data have been generated with time through various *** networks have made the process of data analysis very difficult by detecting attacks using suitable *** Intrusion Detection systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown *** paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion *** experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion *** study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural ***,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly *** focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection *** implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).

关键词： Intrusion detection association rule data mining algorithms KDD99