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Robusta Coffee Bean Defect Classification using Convolutional Neural Network

Robusta Coffee Bean Defect Classification using Convolutiona...

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international conference on Informatics and Computational Sciences (ICICoS)

作者： Ranzel V. Dimaculangan Marife A. Rosales Electronics Engineering Department Polytechnic University of the Philippines Lipa City Batangas Philippines

ISBN: (数字)9798350375886

ISBN: (纸本)9798350375893

Coffee consumption in the Philippines has been continuously growing but the production of green coffee beans has been decreasing since 2015, resulting in demand and supply imbalances. Two of the identified root causes are low yield and poor quality of coffee beans. thus, this study aims to leverage image processing and Convolutional Neural Networks (CNN) to develop a deep learning model that can detect common defects (i.e., black, sour, broken, quaker, and foreign material) of Robusta coffee beans and classify them. With automated defect classification, farmers can use this data and look for trends to identify potential problems during production as coffee bean defects can be attributed to issues during planting and/or cultivation. With such data, farmers will be able to implement preventive measures early on which can eventually lead to higher overall yield and improved quality of coffee beans. Using Python, all programming, model training, and evaluation were developed through Jupyter Notebook. A modified AlexNet CNN architecture was used, and it was optimized through a series of hyperparameter tuning. Based on the results of a five-fold crossvalidation, the optimized AlexNet CNN model has an average classification accuracy of $\mathbf{92.67} \%$, F1-score of $\mathbf{92.68} \%$, and Cohen’s Kappa of $91.20 \%$.

关键词： Training Deep learning Image processing Production programming Market research Convolutional neural networks

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Research on the Application of the Method of System Engineering in Computer Software Design

Research on the Application of the Method of System Engineer...

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2017 7th ICEA international conference on Information Science, Library Science and Social Sciences (ILS 2017)

作者： Hengni Ren College of Information and Engineering Shaanxi Institute of International Trade & Commerce

With the rapid development of computer technology, the computer has gradually become an indispensable part of people's lives. All kinds of programming language of computer have been mastered and used by more people, however, the function is more and more strong, the use is also very convenient, the process of the development of software has also become more complex, so the quality of the software is also very difficult to be guaranteed to a certain extent. And the method of system engineering is mainly aimed at the disadvantages and the solution to the problem in computer development software design. Because of its complexity, great attention must be paid to the application in computer software design. Because of the importance of this problem, at present, there are also many professional articles about the problem of the development of large software project. the purpose of this paper is to improve the quality and efficiency of computer software by studying the method of system engineering.

关键词： Computer software design Method of system engineering Application Research

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Incremental Quaternion Random Neural Networks 7th

Incremental Quaternion Random Neural Networks

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7th international conference on Cognitive Systems and Information Processing, ICCSIP 2022

作者： Cui, Xiaonan Wang, Tianlei Chen, Hao Lei, Baiying Vidal, Pierre-Paul Cao, Jiuwen Machine Learning and I-health International Cooperation Base of Zhejiang Province and Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang Hangzhou310018 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Université Paris Descartes Paris75270 France

ISBN: (纸本)9789819906161

Quaternion, as a hypercomplex number with three imaginary elements, is effective in characterizing three- and four-dimensional vector signals. Quaternion neural networks with randomly generated quaternions as the hidden node parameters become attractive for the good learning capability and generalization performance. In this paper, a novel incremental quaternion random neural network trained by extreme learning machine (IQ-ELM) is proposed. To fully exploit the second-order Q-properness statistic of quaternion random variables, the augmented quaternion vector is further applied in IQ-ELM (IAQ-ELM) for hypercomplex data learning. the network is constructed by gradually adding the hidden neuron one-by-one, where the output weight is optimized by minimizing the residual error based on the fundamental of the generalized HR calculus (GHR) of quaternion variable function. Experiments on multidimensional chaotic system regression, aircraft trajectory tracking, face and image recognition are conducted to show the effectiveness of IQ-ELM and IAQ-ELM. Comparisons to two popular quaternion RNNs, Schmidt NN (SNN) and random vector functional-link net (RVFL), are also provided to show the feasibility and superiority of using quaternions in RNN for incremental learning. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)

Basic Research on Machine Vision Underpinned by Image Frame ...

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international conference on Machine Vision and Information Technology (CMVIT)

作者： Yingxu Wang President International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Visiting Professor: Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018–2022) Distinguished Visiting Professor Tsinghua Univ. (2019–22) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Computer vison [1], [2], [3], [4], [5] studies properties of machine vision, its semantic understanding, and general manipulations by Intelligent Mathematics (IM) [6], [7], [8], [9], [10] [11], [12], [13], [14], [15] [16], [17]. Computer vison has been studies from various aspects such as algorithmic methods, analysis methods, pattern recognitions, and neural-network-regression (AI) technologies [2], [3]. However, there is a lack of fundamental theories for enabling autonomous image recognition and processing by machines. Basic research on contemporary IM has revealed that formal manipulations of visual objects by intelligent machines may be rigorously implemented by Image Frame Algebra (IFA) [8], [18] in the front-end and Visual Semantic Algebra (VSA) [19] in the backend. IFA formally manipulates visual images as general 2D matrixes by a set of algebraic operators such as modeling, analyses, syntheses, feature elicitation, and pattern recognition [4], [5], [18]. then, its counterpart, VSA, transforms the geographic relations of visual objects to their semantic interpretations by algebraic analyses and compositions. the coherent theory of IFA and VSA provides a formal methodology for machine-enabled image processing and comprehension. this keynote presents a theoretical framework of machine vision underpinned by IFA and VSA for the structural denotations of visual objects and functional manipulations of visual mechanisms [3], [8], [9]. It demonstrates how the persistent challenges to machine vision may be rigorously and efficiently solved by the IFA/VSA methodology. Case studies on applying IFA/VSA for rigorous visual pattern detection, recognition, analysis, and composition in real world will be demonstrated [5], [18], [20]. As two coherent paradigms of IM, among others [21], [22], [23], [24], [25] [26], [27], [28], [29], [30], IFA and VSA have been applied not only in robot visual and spatial reasoning, but also in computational intelligence and AI for rigorously

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mpiPython: Extensions of Collective Operations

mpiPython: Extensions of Collective Operations

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international conference on Information and Computer Technologies (ICICT)

作者： Judah Nava Jaden Jinu Lee Hanku Lee Computer Science and Information Systems Minnesota State University Moorhead Moorhead MN

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

Despite performance limitations due to its interpreted nature, Python remains a dominant language among scientists and engineers. Enhancing its capabilities for parallel programming unlocks significant potential within parallel and cloud computing environments. mpiPython, a Python binding for message-passing interfaces, empowers Python for Single Program Multiple Data (SPMD) execution, enabling efficient parallel computations. Additionally, Python's inherent accessibility and versatility foster a growing demand for scaling and parallelizing it on distributed cloud environments. this paper extends mpiPython, bridging the gap in collective operations for parallel computing. the extension builds upon the original mpiPython's class-based structure, emphasizing two core principles: supporting vanilla Python with MPI and focusing on a C-based CPU-focused implementation. Unlike existing implementations like mpi4py, mpiPython directly interacts with the Python C API, offering greater control. Two new functions, MPI Gather and MPI Reduce, significantly improve efficiency and streamline collective operations between working nodes. the results demonstrate mpiPython's ability to perform at the level of other libraries while prioritizing a simple implementation accessible to a broad range of users.

关键词： Cloud computing Technological innovation Reviews Parallel programming Parallel processing Performance gain Libraries

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Joint Transmit and Receive Beampattern Design for Multi-Target Tracking in C-MIMO Radar System

Joint Transmit and Receive Beampattern Design for Multi-Targ...

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international conference on Electronics Technology (ICET)

作者： Lintao Ding Chenguang Shi Kang Chen Jianjiang Zhou Key Laboratory of Radar Imaging and Microwave Photonics Nanjing Univ. Aeronaut. Astronaut Nanjing China Marine Design & Research Institute of China Shanghai China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

In this paper, a joint transmit and receive beampattern design (JTRBD) strategy is developed for multi-target tracking (MT) in colocated multiple-input multiple-output (CMIMO) radar system under hostile environment. the key mechanism of the JTRBD strategy can be divided into two successive iteration optimization stages. First, the C-MIMO radar optimizes the transmit waveform correlation matrix (WCM) to form a low peak sidelobe level (PSL) and resource-awarebased beampattern, thereby minimizing the MT error and the probability of interception. Second, the radar system designs the weights of the spatial filters and employs digital beamforming techniques to generate corresponding receive beampatterns, so as to reduce the gain in the direction of oppressive interference. Due to the coupling of the adaptable parameters in both the objective function and constraints, the resultant problem is nonconvex and NP-hard, and therefore, an iterative solution scheme based on semidefinite programming (SDP) algorithm and the nonmonotone spectral projected gradient (NSPG) method is devised. Simulation results illustrate the effectiveness of the JTRBD strategy.

关键词： Measurement Semidefinite programming Simulation Radar Radar tracking Spatial filters Iterative algorithms

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Comprehensive Approaches to Securing Multi-Cloud Architectures: Best Practices and Emerging Solutions

Comprehensive Approaches to Securing Multi-Cloud Architectur...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Raveena R Nair D. Sreevidya Chinnem Rama Mohan Joydeep Banerjee Kuldeep Chouhan Dharamvir Saveetha School of Law Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of CSE (CyS DS) and AI & DS VNRVJIET Nizampet Hyderabad India Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India NSHM Knowledge Campus Durgapur Kolkata West Bengal India Department of Computer Science Applications Sharda School of Engineering and Technology Greater Noida Uttar Pradesh India Department of MCA The Oxford College of Engineering Bommanahalli Bengaluru Karnataka India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With an emphasis on multi-cloud networks, this research paper offers a synopsis of cloud computing techniques. It covers the fundamentals of cloud programming, the difficulties that cloud computing faces, and the ways that multi-cloud systems, such as cloud hybridization, federation clouds, cross-clouds, and multi-clouds, might help solve these problems. the opportunity of multi-cloud structures in combination with modern technologies such as Big Data and also Machine Learning (ML) is explored as well in the study, leading to some fascinating breakthroughs. To assist researchers, grasp the difficulties and generate enthusiasm within the community for the upcoming pairing of multi-cloud services with additional cutting-edge technologies, the aim is to present a contemporary perspective on multi-cloud technology and solve outstanding concerns.

关键词： Cloud computing Machine learning Computer architecture Companies programming Maintenance engineering Big Data Safety Informatics Faces

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Secure Communications Employing Intelligent Reflecting Surfaces

Secure Communications Employing Intelligent Reflecting Surfa...

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international conference on Electrical, Electronics and System Engineering (ICEESE)

作者： Hsuan-Fu Wang Fang-Biau Ueng Si-Fan Haung Department of Aeronautical Engineering National Formosa University Yunlin Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (数字)9798350367447

ISBN: (纸本)9798350367454

this paper focuses on secure transmission with multiple intelligent reflecting surfaces (IRS) for millimeter-wave (mmWave) and terahertz (thz) systems. Specifically, it explores the scenario where a base station (BS) and a user (D) utilize multi-IRS for transmission while having active eavesdropping from an eavesdropper (E). there is no direct path between the BS and the user, nor between the eavesdropper and BS. Considering hardware implementation, the conditions for discrete phase shifts of IRS reflections are designed. Under these conditions, we maximize the system’s secrecy rate of the mmWave/thz systems. We solve the non-convex problem by decomposing it into two sub-problems: optimizing beamforming and then optimizing the reflection phase shift matrix. then, these sub-problems are jointly optimized. For the beamforming optimization problem, we propose a closed-form optimal solution. For the optimization problem of the reflection phase shift matrix, we propose a method based on semidefinite relaxation programming (SDR) and element-wise block coordinate descent (E-BCD) to optimize the matrix. Simulation results show the effectiveness of the proposed methods.

关键词： Array signal processing Simulation Millimeter wave technology programming Systems engineering and theory Reflection Matrix decomposition Millimeter wave communication Optimization Terahertz communications

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An agent-based electrical power market 08

An agent-based electrical power market

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Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers

作者： Jaime Cerda Jacobo David De Roure Enrico H. Gerding University of Southampton Southampton UK

this demonstration shows an agent-based model for the electricity power market, in which the optimal power flow is determined in a bottom-up fashion. Here, each agent controls a single electrical node consisting of several power generators, loads (consumer demand), and is connected to neighbouring nodes through transmission lines. Furthermore, each of the components has associated physical constraints, such as the line and generators' capacities. through a process resembling tatonnement in markets, the optimal system solution which maximises social welfare is reached within a few iterations. the demonstrator visualises this process and also shows how the various constraints affect the system behaviour and how this changes with different settings.

关键词： electrical power markets distributed constrained optimization quadratic separable programming

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Comparing winner determination algorithms for mixed multi-unit combinatorial auctions 08

Comparing winner determination algorithms for mixed multi-un...

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Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3

作者： Brammert Ottens Ulle Endriss Swiss Federal Institute of Techonology University of Amsterdam

ISBN: (纸本)9780981738123

Mixed multi-unit combinatorial auctions are combinatorial auctions in which the auctioneer and the bidders negotiate over transformations rather than over simple goods. By proposing a transformation a bidder is offering to produce a certain set of output goods after having received the specified input goods. Solving such a mixed auction means choosing a sequence of transformations such that the auctioneer ends up with all the goods desired at the lowest possible cost. this is a generalisation of the winner determination problem in combinatorial auctions and cannot be solved using standard winner determination algorithms. In this paper we analyse the computational complexity of the winner determination problem for mixed auctions and compare the performance of two new algorithms and of the original algorithm proposed for the problem. We also discuss suitable ways of generating test sets for this comparison.

关键词： combinatorial auctions integer programming

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