检索结果-内蒙古大学图书馆

Green Industrial Electronics and Sustainable Technologies (GIEst), IEEE international conference on

作者： Menaka. S Harsh Jain Prithvi Charan B Hemanth P Jeffrey Cris Amanjeet Singh Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram India Department of CSE SRM Institute of Science and Technology Ramapuram India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Remote sensing is one of the important applications for monitoring the ecological conditions in the world. Over the years of climate change and urbanization produced a greater impact on land utilization as compared to its original form. With the help of satellite images like LIDAR, Senitel and Hyperspectral images, the remote sensing process were performed. Among these images, the Hyper-Spectral image have a greater advantage and impact in analyzing the land utilization. Because this HS images has both spectral and spatial properties of the region and it helps to improve the land classification. Several research works also stated that both spatial and spectral properties of HS image is required for analysis and these features were collected using multi-models or individual network. Hence, in this, the land classification using HS images were performed using Attention based modified VGN network. To perform this, the images undergo two main processes namely channel transformation from RGB to LAB and LBP image formation for an efficient feature extraction and classification in A-M-VGN. This proposed method was tested on multi-class like agricultural, barren, green and water bodies region in HS images and evaluated using Accuracy, computation time, Kurtosis, standard Deviation and mean. The Kurtosis, mean and SD helps to analyze the pixel variations in the image. The results showed that the proposed approach is efficient for land classification using HS images.

关键词： Visualization Image color analysis Urban areas Kurtosis Vectors Satellite images standards Optimization Monitoring Hyperspectral imaging Climate change

来源：评论

学校读者我要写书评

暂无评论

Classification of Aflatoxin B1 Contamination Level Using Hyperspectral Images with Random Forest and QDA

Classification of Aflatoxin B1 Contamination Level Using Hyp...

引用

Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Md. Ahasan Kabir Ivan Lee Gayatri Mishra Brajesh Kumar Panda Chandra B Singh Sang-Heon Lee UniSA STEM University of South Australia South Australia Australia Agricultural and Food Engineering Department IIT Kharagpur West Bengal India CARIE Lethbridge College Alberta Canda

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

Almonds are widely accepted nuts due to their taste and healthy nutrition, but they are also susceptible to aflatoxin B1. The current aflatoxin B1 detection method for almonds is destructive, labor intensive, costly, and has sampling problems. Therefore, this study investigates the feasibility of classifying aflatoxin B1 contaminated almonds using hyperspectral images in a non-destructive way for industrial quality control applications. In this experiment, almonds are artificially contaminated at different concentration levels in a laboratory and used as reference levels. The reference level and the mean hyperspectral image were used to develop a random forest (RF) and quadratic discriminant analysis (QDA) classifier. The minimum redundancy maximum relevance (mRMR) feature selection algorithm was introduced to find the most relevant feature spectral set, which was used to develop multispectral classification models for industrial quality control applications. The full spectrum RF classifier achieved 96.37% cross-validation accuracy for standard normal variance (SNV) with 1 st derivative data and QDA achieved 91.71% accuracy for Savitzky-Golay (SG) with 1 st derivative data. The experimental result demonstrates that the hyperspectral images coupled with the machine learning classifier have a great potential to classify aflatoxin B1 contaminated almonds for industrial applications.

关键词： Industries Accuracy Redundancy Quality control Feature extraction Classification algorithms Random forests standards Hyperspectral imaging Classification tree analysis

来源：评论

学校读者我要写书评

暂无评论

Analysis of machine learning Approaches to Detect Pedestrian Under Different Scale Using Frame Level Difference Feature 1st

Analysis of Machine Learning Approaches to Detect Pedestrian...

引用

1st international conference on Computational Sciences and Sustainable Technologies, ICCSst 2023

作者： Sumi, A. Santha, T. Department of Information Technology Avinashilingam Institute for Home Science and Higher Education for Women Tamilnadu Coimbatore India Department of Computer Science Dr. G. R. Damodaran College for College of Science Tamilnadu Coimbatore India

ISBN: (纸本)9783031509926

Over the last 20 years, automotive technology has advanced to the point where automated systems can currently handle various aspects of vehicle control. Due to traffic congestion, pedestrians are particularly vulnerable and they collide with the vehicle’s front end. In recent years, the legal standards and consumer protection assessments for pedestrian protection have gotten much stronger. Sensor technology that must reliably detect an impact between a vehicle and a person has substantial hurdles as a result. Computer vision-based technologies play an important role in the enhancement strategies of automation industries like the Advanced Driver Assistant System (ADAS) by identifying and tracking people on the road. During the process of pedestrian identification, human characteristics are a key factor in determining accuracy. The extraction of features to identify the pedestrians from the video images is a difficult task. In this paper, a per-frame evaluation methodology is taken in hand to make in depth and insightful comparisons among state of art detection techniques with FLD features. This investigates the detection rates at different scales. The experiments that are conducted with the help of Caltech pedestrian dataset. The recognition accuracy is compared and evaluated by avoiding a higher number of erroneous hits and a higher percentage of miss rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Performance Scoring Approach for QoS Optimization in Private 5G Networks

A Performance Scoring Approach for QoS Optimization in Priva...

引用

Emerging Technologies for Dependable Internet of Things (ICETI), international conference on

作者： Seyedali Hadian Axel Sikora Dominik Welte Manuel Schappacher Fabian Sowieja Institute of Reliable Embedded Systems and Communication Electronics (ivESK) Offenburg University of Applied Sciences Offenburg Germany

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Performance benchmarking is crucial for optimizing networks, including 5G Non-Public Networks (5G-NPN). Since one of the major advantages of 5G-NPN is to guarantee Quality of Service (QoS), ensuring optimal performance for their diverse applications is critical. This requires adjustments and testing of various radio-related parameters. Also, performance analysis and benchmarking need to be done based on the evaluation of relevant Key Parameter Indicators (KPIs) in order to identify the parameter set for the optimum performance for each application’s requirements. Many published results on performance benchmarking often lack transparency in their scoring methods. Additionally, QoS benchmarking evaluation for 5G-NPN use cases needs further steps due to the varying ranges of their KPIs. For example Block Error Rate (BLER) is mostly represented by percentage, while Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) are negative values.3GPP in TR 103.559 outlines practices for benchmarking network QoS, with a focus on Speech and multimedia Transmission Quality (stQ). This paper extends these outlines to evaluate 5G-NPN performance by defining a multi-objective function. We select a specific 5G-NPN use case as an example and apply four tests with varying network configurations. After each test, we collect most relevant 5G KPIs. To facilitate comparison, all KPIs are rescaled to a common scale. Additionally, we assign weights to each KPI based on its significance in the chosen use case. By combining rescaling and weight assignments, we propose a single metric that effectively characterizes the overall network performance for 5G-NPNs based on their specific use case requirements.

关键词： Performance evaluation 5G mobile communication Urban areas Modulation Quality of service machine learning Benchmark testing Fourth Industrial Revolution Performance analysis Optimization

来源：评论

学校读者我要写书评

暂无评论

Enhancing Energy Efficiency in UAV Cognitive Radio Networks: A machine learning-Based Optimization Approach

Enhancing Energy Efficiency in UAV Cognitive Radio Networks:...

引用

Emerging Technologies for Dependable Internet of Things (ICETI), international conference on

作者： Mamoon M. Saeed Areeg Ali Elnaim Amin Babeker Mohammed Barakat Mosab Hamdan Elmustafa Sayed Ali Rania A. Mokhtar Department of Communications and Electronics Engineering University of Modern Sciences (UMS) Yemen Alneelain University Hkarotum Sudan School of Elecs Eng. Sudan University of Science and Technology Khartoum Sudan Universiti Malaysia Sabah Kota Kinabalu Malaysia Red Sea University Portsudan Sudan

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

The unmanned aerial vehicle has gained much popularity in these years due to the broad utilization in performing various tasks that range from monitoring, collecting data, and surveillance. However, all these are limited by the battery life of UAVs, which further determines the fly distance and time. Different methods have, therefore, been suggested for the optimization of energy for UAVs. One approach that can be employed to enable energy efficiency in UAVs with dynamic access to available spectrum resources is by considering cognitive radio networks. This article presents the study of UAVs cognitive radio networks for energy efficiency optimization. Since UAVs can offer effective and flexible communication services, their usage in cognitive radio networks has attracted a lot of attention. However, owing to the limited energy resources of UAVs, dependable and sustainable communication is difficult to achieve. This work may provide great insight into the design and optimization of UAV-based cognitive radio networks toward reliable and sustainable communication services. In this paper, we present an energy-efficient optimization strategy taking into consideration the network dynamic structure and different energy usages for the UAVs. The following is proposed in this paper as an algorithm that uses ML techniques for the estimation of energy consumption for UAVs, which then tunes the adjustment of the routing pathways. Simulation results show that the proposed method enhances energy efficiency with constant network performance.

关键词： Energy consumption Maximum likelihood estimation Surveillance Simulation Autonomous aerial vehicles Routing Sensors Cognitive radio Vehicle dynamics Optimization

来源：评论

学校读者我要写书评

暂无评论

Model of neural networks for fertilizer recommendation and amendments in pasture crops 1

Model of neural networks for fertilizer recommendation and a...

引用

1st international conference on Applied Informatics Workshops, ICAIW 2018 - Joint Workshop on Data Engineering and Analytics, WDEA 2018, Workshop on Smart Sustainable Cities, WSSC 2018, Workshop on Intelligent Transportation Systems, WITS 2018 and Workshop on Empirical Experiences on Software Reuse, WEESR 2018

作者： Moreno, Rafael Hernández Garcia, Olmer Luis Alejandro Arias, R. Modeling and Simulation Universidad Jorge Tadeo Lozano Bogotá Colombia Engineering Department Universidad Jorge Tadeo Lozano Bogotá Colombia Department of Biological and Environmental Sciences Universidad Jorge Tadeo Lozano Bogotá Colombia

ISBN: (纸本)9781538694596

In the production of milk, pastures are the basis of bovine feeding, it is considered that around 50% of the costs to produce a liter of milk correspond to the feeding of the cows. Therefore the cultivation of pastures is an essential factor in the dairy industry. The cultivation of pastures requires the preparation of the soil adding fertilizers and amendments for the increase of biomass. The determination of fertilizers and amendments is an activity that adds costs to the cultivation process. The purpose of this work is to identify that from the basic nutrients in the soil such as Nitrogen(N), Phosphorus(P) and Potassium(K), with an MLP neural network of multiple input layers and multiple output layers trained with the back propagation algorithm, it can determine the fertilizers and amendments required by pasture cultivation. The results indicate that despite using a small sample of data (44), with a threshold of 0.75 and with k-fold =3 it was possible to identify the fertilizers Potassium chloride(Y2), Diammonium phosphate(Y6), Copper sulfate(W3) and Zinc sulfate(W4) for the cultivation of pastures. With these results, our contribution is to initiate the use of ML in pasture cultivation and later extend it to other crops. The importance of this work lies in the fact that the results can contribute to the reduction of milk production costs and that the results of this work will be the baseline for the use of other machine learning algorithms in agriculture. © 2018 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Enabling Rapid Data Transfer in Medical IoT Systems with a High-Throughput PRESENT Cipher Hardware Framework

Enabling Rapid Data Transfer in Medical IoT Systems with a H...

引用

Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), international conference on

作者： Uma Reddy Yagateela Pandu Rangaiah Amandeep Nagpal Himanshu Rai Goyal Mandapati Venkata Rama Sundari K. Adnan Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Hyderabad India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of CSE(DS) GRIET Bachupally Hyderabad Telangana India Hilla University College Babylon Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The timely and dependable sharing of essential medical information is dependent on fast and secure data transfer in the arena of Medical Internet of Things (IoT) technologies. In order to facilitate quick data transmission in Medical IoT devices, a high-throughput PRESENT Cipher hardware architecture is presented here. The suggested technique is comprised of various integral parts, such as the design of the hardware framework, the selection of the algorithm, the management of the keys, and the optimization of the performance. Important features of the proposed technique, such encryption, data compression, and error correction, are represented by mathematical equations. At its heart, our method is the PRESENT Cipher Algorithm, which is lauded for its efficiency and its high level of security. This technique is quite effective at encrypting data, protecting it while in transit. Data compression methods like Run-Length Encoding (RLE) are used to decrease data size and improve efficiency. Reed-Solomon codes provide error correction and hence ensure the reliability of transmitted data. Encryption keys are also protected by means of key management and other security procedures. It is determined how successful the suggested approach is by defining and evaluating a variety of performance assessment measures such as encryption speed, energy efficiency, data transmission rate, latency, resource usage, data integrity, compatibility, and scalability. The results show that the suggested technique is superior to the status quo methods in many respects. It is well-suited to the requirements of Medical IoT systems because to its faster encryption speed, reduced energy usage, larger data transmission speeds, and enhanced data integrity.

关键词： Reed-Solomon codes Ciphers Data integrity Data compression Medical services Data transfer Hardware Encryption Internet of Things Reliability

来源：评论

学校读者我要写书评

暂无评论

A Deep learning Based Bio Fertilizer Recommendation Model Based on Chlorophyll Content for Paddy Leaves 1st

A Deep Learning Based Bio Fertilizer Recommendation Model Ba...

引用

1st international conference on Computational Sciences and Sustainable Technologies, ICCSst 2023

作者： Nirmala Devi, M. Siva Kumar, M. Subbulakshmi, B. Uma Maheswari, T. Karpagam Vasanth Kumar, M. Department of Computer Science and Engineering Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (纸本)9783031509926

Rice is the main agricultural product in India, with 90% of the population consuming it as a staple food. Nutrient deficiencies in rice leaves during growth period causes imbalances leading to reduce in crop yield. Growth of the paddy plants is highly related to the chlorophyll content present in it. Chlorophyll content in a leaf is a key indicator of greenness of a leaf and identifies the nutrient deficiencies in plants. Chlorophyll in plants contains nitrogen which is responsible for the photosynthesis process. In this research, chlorophyll and nitrogen contents are measured for the paddy leaves. Here, Support Vector machine (SVM) Regression and Convolutional Neural Network (CNN) models are used to measure the chlorophyll and nitrogen content in plants. In this research, the color was the main parameter used to quantify chlorophyll and nitrogen contents in plants and RGB color model is used. Bio fertilizers is an influencing factor in yield progression and physiological processes as Bio fertilizers supply necessary nutrients to plants and enhance chlorophyll content in leaves. The chlorophyll and nitrogen concentrations were measured and then based on the measured nitrogen concentration, the appropriate bio fertilizer is recommended by the SVM classification model to enhance the nitrogen content in the plants. Here the CNN (99.98%) algorithm works better than the SVM algorithm in prediction of Chlorophyll and nitrogen contents. Hence, the Convolutional Neural Network model is built to predict the chlorophyll and nitrogen contents for the paddy leaves based on color and recommends the appropriate bio fertilizer to improve plant growth. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

1st international conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021)

引用

Journal of Physics: conference Series 2021年第1期1950卷

PREFACE It was a matter of pleasure to organize 1st international conference on 'Mechatronics and Artificial Intelligence' (ICMAI-2021) at Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram on 26th and 27th February 2021. The conference was organized in Association of Engineering & Technocrats, Faculty of Engineering and Technology, SGT University. The objective of this conference was to provide a common platform to scientists and researchers from all over India for exchanging their knowledge and views to deal with global challenges. The theme of this conference was not only the fundamental technology in the field of engineering science but also can change our society by exploring new horizons and continuous progress. A diverse range of topics from the neural network, digital image processing, machine learning, programmable logic controller, health care, IoT based systems, deep learning, lean manufacturing, abrasive flow machining, welding technology and simulation-based studies are featured in the conference. In addition to that, you will also hear about the incremental forming, automation integration techniques, wireless sensor techniques, non-invasive methods of diagnosis, robot assembly, data mining and various evolutionary algorithms in different applications. I hope, therefore, that you will get a chance to explore recent emerging technologies. We are delighted to deliver special thanks to IOP Publishing as our publication partner to publish the conference proceedings in JPCS. We sincerely thanks to our sponsors Council of Scientific and Industrial Research (CSIR) Human Resource Development Group (HRDG) and 'Sanranchana', SGT University Center of Research Innovation. We believe that the conference had provided the opportunity to the students and the young researchers for enriching their knowledge through interaction with eminent researchers from various NITs, IITs, state universities and reputed organizations. We hope that everybody had a great ex

关键词：

来源：评论

学校读者我要写书评

暂无评论

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference

引用

EUROPACE 2024年第4期26卷 euae070页

作者： Linz, Dominik Andrade, Jason G. Arbelo, Elena Boriani, Giuseppe Breithardt, Guenter Camm, A. John Caso, Valeria Nielsen, Jens Cosedis De Melis, Mirko De Potter, Tom Dichtl, Wolfgang Diederichsen, Soren Zoega Dobrev, Dobromir Doll, Nicolas Duncker, David Dworatzek, Elke Eckardt, Lars Eisert, Christoph Fabritz, Larissa Farkowski, Michal Filgueiras-Rama, David Goette, Andreas Guasch, Eduard Hack, Guido Hatem, stephane Haeusler, Karl Georg Healey, Jeff S. Heidbuechel, Hein Hijazi, Ziad Hofmeister, Lucas H. Hove-Madsen, Leif Huebner, Thomas Kaab, stefan Kotecha, Dipak Malaczynska-Rajpold, Katarzyna Merino, Jose Luis Metzner, Andreas Mont, Lluis Ng, Ghulam Andre Oeff, Michael Parwani, Abdul Shokor Puererfellner, Helmut Ravens, Ursula Rienstra, Michiel Sanders, Prashanthan Scherr, Daniel Schnabel, Renate Schotten, Ulrich Sohns, Christian steinbeck, Gerhard steven, Daniel Toennis, Tobias Tzeis, stylianos van Gelder, Isabelle C. van Leerdam, Roderick H. Vernooy, Kevin Wadhwa, Manish Wakili, Reza Willems, stephan Witt, Henning Zeemering, stef Kirchhof, Paulus Maastricht Univ Med Ctr Cardiovasc Res Inst Maastricht Dept Cardiol Maastricht Netherlands Univ Copenhagen Fac Hlth & Med Sci Dept Biomed Sci Copenhagen Denmark Vancouver Gen Hosp Div Cardiol Vancouver BC Canada Montreal Heart Inst Montreal PQ Canada Univ Barcelona Hosp Clin Inst Clin Cardiovasc Barcelona Spain Inst Invest Biomed August Pi i Sunyer IDIBAPS Barcelona Catalonia Spain Ctr Invest Biomed Red Enfermedades Cardiovasc CIBE Madrid Spain European Reference Network Rare Low Prevalence & C I-40138 Bologna Italy Univ Modena & Reggio Emilia Dept Biomed Metab & Neural Sci Cardiol Div Polyclin Modena Modena Italy Univ Hosp Muenster Dept Cardiovasc Med Munster Germany Atrial Fibrillat Network AFNET Munster Germany St Georges Univ London Cardiol Clin Acad Grp Mol & Clin Sci Inst London England Univ Perugia Santa Maria della Misericordia Hosp Stroke Unit I-06100 Perugia Italy Aarhus Univ Hosp Dept Cardiol Aarhus Denmark Aarhus Univ Dept Clin Med Aarhus Denmark Bakken Res Ctr Maastricht Netherlands Onze Lieve Vrouw Hosp Cardiovasc Ctr Aalst Belgium Med Univ Innsbruck Dept Internal Med Cardiol & Angiol 3 Innsbruck Austria Copenhagen Univ Hosp Rigshosp Dept Cardiol Copenhagen Denmark Univ Duisburg Essen Univ Hosp Essen Inst Pharmacol Fac Med Essen Germany Schuchtermann Klin Dept Cardiac Surg Bad Rothenfelde Germany Hannover Med Sch Hannover Heart Rhythm Ctr Dept Cardiol & Angiol Hannover Germany Pfizer Pharm GmbH Berlin Germany Univ Hosp Munster Dept Cardiol Electrophysiol 2 Munster Germany Preventicus GmbH Jena Germany UKE Univ Ctr Cardiovasc Sci UHZ Hamburg Germany German Ctr Cardiovasc Res DZHK Partner Site Hamburg Kiel Lubeck Hamburg Germany Univ Birmingham Inst Cardiovasc Sci Birmingham England Minist Interior & Adm Dept Cardiol Natl Med Inst Warsaw Poland Ctr Nacl Invest Cardiovasc CNIC Novel Arrhythmogen Mech Program Madrid Spain Hosp Univ Clin San Carlos IdISSC Inst Invest Sanitaria Cardiovasc Inst C Prof Mart

Aims Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA).Methods and results Eighty-three international experts met in Munster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with *** Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF. Graphical Abstract

关键词： Atrial fibrillation Artificial intelligence Biomarkers Heart failure Atrial cardiomyopathy Cognitive function Dementia Outcomes Quality of care Cost Research Rhythm management Catheter ablation Anticoagulation Bleeding Research priorities Technology stroke Integrated care Screening AFNET EHRA Guidelines Consensus statement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：