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CLUSTERING ANALYSIS OF DEBRIS-FLOW STREAMS

International Journal of Sediment Research 2004年第1期19卷 37-46页

作者： Yuan-Fan TSAI,Huai-Kuang TSAI,Cheng-Yan KAO Professor of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Assistant Professor of Dept. of Social Studies Education National Taipei Teachers College Taipei 106 Taiwan Post doctoral fellow of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

The Chi-Chi earthquake in 1999 caused disastrous landslides, which triggered numerous debris flows and killed hundreds of people. A critical rainfall intensity line for each debris-flow stream is studied to prevent such a disaster. However, setting rainfall lines from incomplete data is difficult, so this study considered eight critical factors to group streams, such that streams within a cluster have similar rainfall lines. A genetic algorithm is applied to group 377 debris-flow streams selected from the center of an area affected by the Chi-Chi earthquake. These streams are grouped into seven clusters with different characteristics. The results reveal that the proposed method effectively groups debris-flow streams.

关键词： Debris flow Genetic algorithm hierarchical clustering The Chi-Chi earthquake

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Learning sparse representation for leaf image recognition

Learning sparse representation for leaf image recognition

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chao-Yung Hsu Chia-Yen Chen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Institute of Information Science Taipei Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. For each test leaf image, we calculate the correlation between the image and each learned dictionary of leaf species to achieve the identification of the leaf image. As a result, efficient leaf recognition can be achieved on public leaf dataset based on the proposed framework leading to a more compact and richer representation of leaf images compared to traditional clustering approaches. Moreover, our method is also adapted to newly added leaf species without retraining classifiers and suitable to be highly parallelized as well as integrated with any leaf image descriptors/features.

关键词： Image recognition Dictionaries Training Feature extraction Shape Educational institutions computer vision

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Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel lea...

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science and information Conference (SAI)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chih-Yang Lin Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

关键词： Image recognition Dictionaries Training Feature extraction Shape Vectors Image coding

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis 16th

Collaborative Learning of Gesture Recognition and 3D Hand Po...

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16th European Conference on computer Vision, ECCV 2020

作者： Yang, Siyuan Liu, Jun Lu, Shijian Er, Meng Hwa Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783030585792

Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art. © 2020, Springer Nature Switzerland AG.

关键词： Gesture recognition

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Fuzzy Fusion for Affective State Assessment in Distance Learning Based on Image Detection

Fuzzy Fusion for Affective State Assessment in Distance Lear...

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2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)

作者： Kuoan Hwang Chiahao Yang Department of Computer Science and Information Engineering & Graduate Institute of Networking and Co Graduate Institute of Informatics Doctoral program Chaoyang University of Technology

Distance learning is a learning style that can overcome the limitation of time and space. Because of the distance, teachers can not handle the student's learning situation, and they do not know whether the student is attentive, drowsy or absent. If teachers can know the student's affective state, they can overcome the difficult. The research applies the image recognition technologies to capture the face images of students when they are learning and analyzes their face features to evaluate the student's affective state by Fuzzy Integral. Finally, teachers can monitor the student's behavior by the detection results on the system interface.

关键词： Distance learning Skin computers Distance measurement Face Image color analysis Education

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Hd Maps for Autonomous Vehicles: Challenges and Representing Frameworks

SSRN

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SSRN 2023年

作者： Al-Saidi, Muslem Aron, Ballagi Szechenyi Istvan University Doctoral School of Multidisciplinary Engineering Sciences Hungary Wasit University College of Computer Science and Information Technology Iraq

As a result of growing the technology in artificial intelligence and robotics, particularly the Highly Automated Driving (HAD) field, many companies, and researchers have focused on this area. thus, it is necessary to provide maps of high quality in terms of the information they contain, as they are very important for researchers in the various fields of research in HAD. A group of commercial companies provided high-quality maps which are used in many scientific types of research. These maps provide very clear and useful information that enables the self-driving vehicle to build approved and shortened paths and avoid various obstacles to facilitate the process of reaching the goal. Otherwise, there is worthless information because the large amount of data within the maps may need more time to process, especially if we know that the information required in HAD is only for a certain distance around the vehicle. On the other hand, there is information that is not available in the maps which may have high importance in HAD. Usually, this type of data is very important, especially in complex city scenarios. Therefore, the authors worked to customize these maps, almost by using the same technology, by self-created map format to meet their research needs. In this paper, we will investigate a group of these maps and touch on their advantages and disadvantages, and we will discuss some challenges of creating maps. © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Research on Feature Extraction and Clustering of Virtual Learning Community Network Learning

Research on Feature Extraction and Clustering of Virtual Lea...

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International Conference on information System and Artificial Intelligence (ISAI)

作者： Yan Cheng Jian-Hua Xie Ping-Fei Tan Zhi-Ming Yang College of Computer and Information Engineering Jiangxi Normal University Nanchang China Post doctoral mobile station of computer science and technology Tongji University Shanghai China

ISBN: (纸本)9781509015863

Virtual learning community is a kind of learning environment based on network is a new type of learning organization. However, Virtual Learning Community in the teaching data is often messy, fragmentary, it's value is often difficult to be detected and reasonable to use data mining techniques to deal with data will give us a analysis to study the effect of get twice the result with half the effort. Personalized teaching has always been a difficult point in the network teaching research, and the extraction and analysis of the characteristics of the learner's learning is the basis of personalized teaching. The purpose of this paper is through the acquisition learning in virtual learning community of learning behavior record and analysis learners learning characteristics, through the establishment of learning feature vectors, using fuzzy c-means algorithm for mining learners common characteristics. The common characteristics of individual learners as a research object of the transformation to the learner that clustering center feature vector as the research object, so that learning features simplified. Characteristics similar to the learner to divide into a group, formulated by the relevant experts in the field of practical teaching strategies and learning tasks, for each group are for teaching, personalized teaching, so as to improve the learning efficiency. Development of individualized instruction.

关键词： Education Collaborative work Algorithm design and analysis Clustering algorithms computers Learning (artificial intelligence) Classification algorithms

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Are Halal Food Supply Chains Sustainable: A Review And Bibliometric Analysis

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Journal of Foodservice Business Research 2021年第5期24卷 554-595页

作者： Rejeb, Abderahman Rejeb, Karim Zailani, Suhaiza Department of Logistics and Forwarding Doctoral School of Regional Sciences and Business Administration Széchenyi István University Györ Hungary Department of Software Engineering Higher Institute of Computer Science El Manar Ariana Tunisia Department of Operations Management and Information System Faculty of Business and Accountancy University Malaya Kuala Lumpur Malaysia

Halal food supply chains (HFSCs) are increasingly characterized by fragmentation, complexity, and uncertainty. Halal food consumers are becoming more demanding for food quality and compliance with Islamic dietary law. As sustainability is a multidimensional concept that entails several aspects and interpretations, the objectives of HFSCs significantly overlap with the sustainable dimensions of the food ecosystem. The purpose of this study is to examine the inherent sustainable aspects and characteristics of HFSCs. Therefore, a systematic literature review and a bibliometric analysis were conducted to redress the lack of academic attention being given to the relationships of HFSCs with sustainability. Selected 74 papers were coded and analyzed according to the dimensions of sustainability, namely, the economic, social, and environmental dimensions. The review primarily attempts to provide answers to the following research question: What are the sustainable aspects of HFSCs? We provide a comprehensive analysis of the three critical components of sustainability, offering a broad and comprehensive picture of many critical issues in HFSCs. Finally, the contribution of the study, the scope of future research, and the limitations are highlighted in detail. © 2021 Taylor & Francis.

关键词： economic Halal Food Supply Chains social sustainability

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SOME TOPOLOGICAL PROPERTIES OF ROUGH APPROXIMATION SPACES

SOME TOPOLOGICAL PROPERTIES OF ROUGH APPROXIMATION SPACES

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： WEI-JIANG LIU QUAN JING LEI BAI Post Doctoral Station for Computer Science and Technology Southeast University Nanjing Jiangsu 2 School of Information Science and Engineering Bohai University Jinzhou 121003 China

General topology has been introduced into the realm of rough sets. In this paper, the topological properties of map on generalized rough approximation space on binary relation are discussed. We point out that a map between a generalized rough approximation space and a finite set can induce a binary relation on the finite set. If the binary relation is reflective and transitive, then the map is continuous which is regarded as a map on topological space. Furthermore, if the map is bijective, then it is simultaneously open and closed, and image and inverse image of a rough set are rough.

关键词： Rough sets topological space binary relation continuous map

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