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检索条件"机构=Computer System Design and Engineering"

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Memetic clustering based on particle swarm optimizer and K-means

Memetic clustering based on particle swarm optimizer and K-m...

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Congress on Evolutionary Computation

作者： Zexuan Zhu Wenmin Liu Shan He Zhen Ji Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Birmingham Birmingham UK

ISBN: (纸本)9781467315104

This paper proposes an efficient memetic clustering algorithm (MCA) for clustering based on particle swarm optimizer (PSO) and K-means. Particularly, PSO is used as a global search to allow fast exploration of the candidate cluster centers. PSO has strong ability to find high quality solutions within tractable time, but it suffers from slow-down convergence as the swarm approaching optima. K-means, achieving fast convergence to optimum solutions, is utilized as local search to fine-tune the solutions of PSO in the framework of memetic algorithm. The performance of MCA is evaluated on four synthetic datasets and three high-dimensional gene expression datasets. Comparison study to K-means, PSO, and PSO-KM (jointed PSO and K-means) indicates that MCA is capable of identifying cluster centers more precisely and robustly than the other counterpart algorithms by taking advantage of both PSO and K-means.

关键词： Clustering algorithms Convergence Gene expression Particle swarm optimization Memetics Educational institutions Acceleration

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Hierarchical alternating least squares algorithm with Sparsity Constraint for hyperspectral unmixing

Hierarchical alternating least squares algorithm with Sparsi...

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IEEE International Conference on Image Processing

作者： Sen Jia Yuntao Qian Jiming Li Yan Li Zhong Ming Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science University of Zhejiang Hangzhou China

In this paper, we not only extend the temporal hierarchical alternating least squares (HALS) to spatial domain, but also incorporate two necessary characteristics of material abundances, full additivity and sparsity, to unmix hyperspectral data. The new algorithm is abbreviated as HALSSC (HALS with Sparsity Constraint). Different from the other endmember extraction approaches, the proposed algorithm does not need the existence assumption of pure pixel of each endmember in the scene. Experimental results on highly mixed synthetic data and real hyperspectral data from Washington DC mall confirm the accuracy of the developed algorithm.

关键词： Hyperspectral imaging Pixel Signal processing algorithms Data mining Reflectivity Materials

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AI-Assisted Markerless Activity Tracking system for Supporting Aging and Wellness

AI-Assisted Markerless Activity Tracking System for Supporti...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Alexander Wong Jennifer Boger George Shaker Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Public Health Sciences University of Waterloo Waterloo Canada System Design Engineering University of Waterloo Waterloo Canada

This paper presents a mm-Wave radar-based system to create a novel method of tracking seniors and monitoring them using ambient wireless signals. Radar sensors, coupled with deep learning models, facilitate the identification of various physical activities without requiring physical contact or the use of wearable devices. The system employs range-Doppler maps derived from a real-life in-home activities dataset for training deep learning networks. The gated recurrent units (GRU) model is chosen for real-time implementation due to its optimal trade-off between speed and accuracy. The system achieves an overall accuracy of 93% for trained subjects, with 86% accuracy for classifying the in-home physical activities of a new subject. Additionally, the system captures the frequency of washroom use, sedentary, duration of sleep, active and out-of-home periods, the level of activity performed by the subject over a period of time, current activity state, and gait parameters.

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Manufacturing A Low-Cost Telegram and Optical Character Recognition-Based Indoor Air Quality Monitoring Data Logger 8

Manufacturing A Low-Cost Telegram and Optical Character Reco...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Pramono, Andi Budiman, Satrio Arif Farchan, Muchammad Mappaturi, Andi Baso School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Brawijaya University Information System Computer Science Faculty Malang65145 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia Islamic State University of Maulana Malik Ibrahim Faculty of Science and Technology Architecture Department Malang Indonesia

ISBN: (纸本)9798350326093

Urban air is frequently contaminated with CO, CO2, VOC, HCHO, PM 2.5, and PM 10. Rural regions are at a lower risk than those near roads and industrial areas that produce emissions. Air pollutants negatively affect its inhabitants' health, particularly the respiratory system. Most people will spend more time indoors, requiring a method for monitoring indoor air quality (IAQ). Urban communities need IAQ monitoring instruments as an early warning system for their health. This research seeks to produce inexpensive IAQ that can be utilized by all levels of society, particularly urban communities. This research utilizes a qualitative, design-thinking-based methodology. The research diverged from Empatize, which asserts that urban communities require a method for determining the local air quality. Moreover, it is determined at the define stage that IAQ monitoring, which includes a data log feature, is still comparatively expensive, so a solution is developed at the ideate stage. Creating a prototype is the next step in realizing the specified solution. In this investigation, they decided to use IAQ monitoring, which is relatively inexpensive but lacks log data storage. The author uses the ESP32-cam microcontroller to capture the screen display device, and the resulting image is sent to a telegram and processed with optical character recognition (OCR) to generate a data log. The results of prototype testing are utilized for prototype development. © 2023 IEEE.

关键词： Industrial emissions

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Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading

Differentially Private Deep Q-Learning for Pattern Privacy P...

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IEEE International Conference on Communications (ICC)

作者： Shuying Gan Marie Siew Chao Xu Tony Q.S. Quek School of Information Engineering Northwest A&F University Yangling Shaanxi China Department of Electrical and Computer Engineering Carnegie Mellon University USA Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications. However, attackers may eavesdrop on the offloading decisions to infer the edge server's (ES's) queue information and users' usage patterns, thereby incurring the pattern privacy (PP) issue. Therefore, we propose an offloading strategy which jointly minimizes the latency, ES's energy consumption, and task dropping rate, while preserving PP. Firstly, we formulate the dynamic computation offloading procedure as a Markov decision process (MDP). Next, we develop a Differential Privacy Deep Q-learning based Offloading (DP-DQO) algorithm to solve this-problem while addressing the PP issue by injecting noise into the generated offloading decisions. This is achieved by modifying the deep Q-network (DQN) with a Function-output Gaussian process mechanism. We provide a theoretical privacy guarantee and a utility guarantee (learning error bound) for the DP-DQO algorithm and finally, conduct simulations to evaluate the performance of our proposed algorithm by comparing it with greedy and DQN-based algorithms.

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Survival analysis of gene expression data using PSO based radial basis function networks

Survival analysis of gene expression data using PSO based ra...

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Congress on Evolutionary Computation

作者： Wenmin Liu Zhen Ji Shan He Zexuan Zhu Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Birmingham Birmingham UK

ISBN: (纸本)9781467315104

Gene expression data combined with clinical data has emerged as an important source for survival analysis. However, gene expression data is characterized with thousands of features/genes but only tens or hundreds of observations. The high-dimensionality and unbalance between features and samples pose big challenges for the classical survival analysis methods. This paper proposes a particle swarm optimization based radial basis function networks (PSO-RBFN) for the survival analysis on gene expression data. Particularly, PSO-RBFN applies a principle component analysis for dimensionality reduction and optimizes the RBF network using PSO. The experimental results on three gene expression datasets indicate that PSO-RBFN is able to improve the predict accuracy compared to the other classical survival analysis methods.

关键词： Gene expression Hazards Training Neurons Data models Principal component analysis Breast cancer

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An Initial Investigation into the Use of Social Robots within an Existing Educational Program for Students with Learning Disabilities

An Initial Investigation into the Use of Social Robots withi...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Negin Azizi Shruti Chandra Mike Gray Melissa Sager Jennifer Fane Kerstin Dautenhahn Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Learning Disabilities Society Vancouver BC Canada Department of System Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Students with a learning disability (LD) generally require supplementary one-to-one instruction and support to acquire the foundational academic skills learned at school. Because learning is more difficult for students with LD, students can frequently display off-task behaviours to avoid attempting or completing challenging learning tasks. Re-directing students back to their learning task is a frequent strategy used by educators to support students. However, there have been limited studies investigating the use of assistive technology to support student re-direction, specifically in a "real-world" educational setting. In this in situ study, we investigate the impact of integrating socially assistive robot to provide re-direction strategies to students. A social robot, QT, was employed within the existing learning program during one-to-one remedial instruction sessions. The study comprised two phases, "Instruction as usual" (IAU) and "Robot-mediated instructions" (RMI). Both followed the students' one-to-one instructional program where students get personalised learning support from their instructors, except for the RMI phase which included a social robot as a tool. We investigated the impact of the robot on students' on-task behaviours and progress towards learning goals. The results of our mixed method analysis suggest that the robotic intervention supported students in staying on-task and completing their learning goal.

关键词： Educational programs Social robots Buildings Assistive robots Task analysis

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On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D 11th

On the Parameter Setting of the Penalty-Based Boundary Inter...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Wang, Zhenkun Deng, Jingda Zhang, Qingfu Yang, Qite School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China

ISBN: (纸本)9783030720612

Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. The subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Sensor network scheduling algorithm considering estimation error variance and communication energy

Sensor network scheduling algorithm considering estimation e...

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IEEE Conference on Control Technology and Applications (CCTA)

作者： Takashi Takeda Toru Namerikawa Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Department of System Design Engineering Keio University Yokohama Japan

This paper deals with a sensor scheduling algorithm considering estimation error variance and communication energy in sensor networked feedback systems. We propose a novel decentralized estimation algorithm with unknown inputs in each sensor node. Most existing works deal with the sensor network systems as sensing systems and it is difficult to apply them to the real physical feedback control systems Then some local estimates are merged and the merged estimates can be optimized in the proposed method and the estimation error covariance has a unique positive definite solution under some assumptions. Next, we propose a novel sensor scheduling algorithm in which each sensor node transmits information. A sensor node uses energy by communication between other sensor node or the plant. The proposed algorithm achieves a sub-optimal network topology with minimum energy and a desired variance. Experimental results show an effectiveness of the proposed method.

关键词： Network topology Estimation error Equations Covariance matrix Scheduling algorithm Silicon

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Feature Selection Technique for Hyperspectral Imagery Classification with Noise Reduction Preprocessing

Feature Selection Technique for Hyperspectral Imagery Classi...

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Chinese Conference on Pattern Recognition (CCPR)

作者： Sen Jia Zhen Ji Zexuan Zhu Yuntao Qian Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science University of Zhejiang Hangzhou China

The rich information available in hyperspectral imagery has posed significant opportunities for material classification and identification. The main problem encountered with the classification process is the high dimensionality of hyperspectral data and the low-sized training dataset. Hence, dimensionality reduction is often adopted to avoid the "curse of dimensionality" phenomenon. However, noise generated by various sources (primarily the sensor and the atmosphere) inevitably decrease the precision of the classifier. In this paper, two wavelet-based methods, wavelet shrinkage and discrete wavelet transform, are applied to preprocess the hyperspectral imagery in sequence, denoising the spatial images and spectral signatures, respectively. After that, affinity propagation, which is a recently proposed feature selection approach, is used to choose representative features from the noise-reduced data. Experimental results demonstrate that the features acquired by the new scheme make the classification results more accurate than those without noise reduction preprocessing.

关键词： Hyperspectral imaging Discrete wavelet transforms Noise reduction Noise Accuracy

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