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International Conference on Applied System Innovation (ICASI)

作者： Wen-Chuan Wu Ming-Han Lee Dep. of Computer Science and Information Engineering National Ilan University Yilan City Taiwan R.O.C. Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan City Taiwan R.O.C.

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

Digital content industry, encompassing areas such as digital games, computer animation, digital audio-visual applications, and digital art, plays a pivotal role in today's and future developmental trends. Maya 3D software stands out as a crucial development tool in these domains. However, the multitude of built-in tools in Maya 3D can lead to overly complex interfaces, impacting development efficiency for software developers. To address this issue, this paper designs a digital toolkit for Maya 3D software, presented in a Menu Bar format. This toolkit offers features like naming, sorting, categorization, and automatic saving to JSON files for future use. Through a crossover experimental design, this paper investigates the impact of this proposed toolkit on 3D software developers' individual work performance, work engagement, and stress perception. Results shows a significant positive influence of that toolkit on software developers, particularly in enhancing work performance. It means that the proposed digital toolkit effectively improves the performance of software developers' work and reduces their stress during the software development process.

关键词： Industries Technological innovation Three-dimensional displays Market research Software Stress Software development management

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Separating composite signals in multi-probe dynamic biomedical imaging

Separating composite signals in multi-probe dynamic biomedic...

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Asilomar Conference on Signals, Systems & computers

作者： Li Chen Yue Wang Chong-Yung Chi Zsolt Szabo Peter L. Choyke Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington VA USA Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Department of Radiology Johns Hopkins Medical Institutions Baltimore MD USA Molecular Imaging Program National Cancer Institute Bethesda MD USA

Multi-probe dynamic biomedical imaging promises powerful tools for the visualization and elucidation of complex biological processes. Recent research aims to simultaneously dissect the spatial-temporal distributions of source signals that often represent a composite of multiple biomarkers independent of spatial resolution. We report a hybrid unmixing method for separating non-negative dependent imaging biomarker mixtures. The geodesic-principled algorithm exploits partial-volume modeling, non-negative clustered component analysis, and convex pyramid analysis, aided by a spatial-temporal coordinated information visualization aid. We demonstrate the principle of the approach on dynamic contrast-enhanced magnetic resonance imaging data and observed the expected vascular permeability and perfusion patterns due to tumor-induced angiogenesis and responses to therapy.

关键词： Biomedical imaging Biomarkers Magnetic resonance imaging Independent component analysis Information analysis Magnetic analysis Algorithm design and analysis Visualization Biological processes Spatial resolution

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Noise-robust deep spiking neural networks with temporal information

arXiv

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arXiv 2021年

作者： Park, Seongsik Lee, Dongjin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of Asri and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the devices are susceptible to noise, which hinders them from being applied in real-world applications. Several studies have increased noise robustness, but most of them considered neither deep SNNs nor temporal information. In this paper, we investigate the effect of noise on deep SNNs with various neural coding methods and present a noise-robust deep SNN with temporal information. With the proposed methods, we have achieved a deep SNN that is efficient and robust to spike deletion and jitter. © 2021, CC BY.

关键词： Deep neural networks

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Wind Energy Projection for the Philippines based on Climate Change Modeling

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Energy Procedia 2014年 52卷 26-37页

作者： Angeli Silang Sherdon Niño Uy Julie Mae Dado Faye Abigail Cruz Gemma Narisma Nathaniel Libatique Gregory Tangonan Ateneo Innovation Center Ateneo de Manila University Quezon City 1108 Philippines Electronics Computer and Communications Engineering Department Ateneo de Manila University Quezon City 1108Philippines Manila Observatory Quezon City 1108 Philippines Atmospheric Science Program Department of Physics Ateneo de Manila University Quezon City 1108Philippines

To complement the existing method of wind energy assessment, this study presents wind energy projection by downscaling a regional climate model, RegCM3, which is also used in predicting rainfall and temperature changes, and using a conversion method using the Weibull distribution. A couple of papers which used long-term predicting models focused on two regions, China and the US High Plains, show a decrease of about 14% and 7%-17% respectively in wind power density due to global warming over the next century. This paper focuses on a smaller grid size of 10 km x 10 km to concentrate on a specific wind farm in Pililla, Rizal, Philippines which is considered as a commercially feasible site by wind developers. Wind energy projection that considers the effects of climate change for the expected period of operation of 25 years is used because this gives wind developers an outlook on the power production during the wind farm's lifetime and would contribute in determining the wind farm's potential for financial returns. Percentage difference of wind power density between the baseline period of 2008-2012 and five-year projection periods from 2013-2037 are presented. Contrary to the results of studies in China and western US, the results of this research show that there is an average five-year period increase of 6% in wind power density in Pililla, Rizal over the next 25 years.

关键词： wind energy assessment climate change wind projection

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Training energy-efficient deep spiking neural networks with time-to-first-spike coding

arXiv

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arXiv 2021年

作者： Park, Seongsik Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of ASRI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the human brain, have been studied as prominent energy-efficient neural networks. Due to their event-driven and spatiotemporally sparse operations, SNNs show possibilities for energy-efficient processing. To unlock their potential, deep SNNs have adopted temporal coding such as time-to-first-spike (TTFS) coding, which represents the information between neurons by the first spike time. With TTFS coding, each neuron generates one spike at most, which leads to a significant improvement in energy efficiency. Several studies have successfully introduced TTFS coding in deep SNNs, but they showed restricted efficiency improvement owing to the lack of consideration for efficiency during training. To address the aforementioned issue, this paper presents training methods for energy-efficient deep SNNs with TTFS coding. We introduce a surrogate DNN model to train the deep SNN in a feasible time and analyze the effect of the temporal kernel on training performance and efficiency. Based on the investigation, we propose stochastically relaxed activation and initial value-based regularization for the temporal kernel parameters. In addition, to reduce the number of spikes even further, we present temporal kernel-aware batch normalization. With the proposed methods, we could achieve comparable training results with significantly reduced spikes, which could lead to energy-efficient deep SNNs. © 2021, CC BY.

关键词： Energy efficiency

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Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

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A Simple Technique to Analysis of Microwave Absorbers using Frequency Selective Surfaces

A Simple Technique to Analysis of Microwave Absorbers using ...

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SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference

作者： Eliel Poggi dos Santos Antonio Luiz P. S. Campos Graduate Program in Electrical and Computer Engineering Federal University of Rio Grande do Norte Department of Engineering Communications Federal University of Rio Grande do Norte

ISBN: (纸本)9781467394932

This work aims to analyze the variation of some parameters in the design of microwave absorbers using FSS. The methodology consists of a combination of two techniques: the equivalent circuit method, which provides the transmission and reflection characteristics of the individual structures for a plane wave with normal incidence, and the scattering matrix technique, which provides the result of two FSS cascaded, a conductive FSS and a resistive FSS, getting absorption properties in the projected range. Simulated results are presented and comparison with results obtained with commercial software are made.

关键词： Simple Technique Analysis Microwave Absorbers Frequency Selective Surfaces

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Ziyue Wang Hibiki Okamura Shijie Luo Yu Hayashi Hotaka Takizawa Master's Program in Computer Science University of Tsukuba Tsukuba Japan Ph.D. Program in Humanics International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing & Communications Center University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3‘-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and $1\times 1$ convolution techniques. Our U-Net m odel, called $1\times 1$ conv U-Net, was applied to three DAB- strainedmouse slice images whose resolutions were $1000\mathrm{x}1000$ pixels. The experimental results demonstrated that the $1\times 1$ conv U-Net outperformed the conventional U-Net.

关键词： Proteins Visualization Image resolution Image analysis Convolution Feature extraction Brain modeling Biology Biomedical imaging

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Construction of time-stamped mobility map for path tracking via smith-waterman measurement matching

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Mathematical Problems in engineering 2014年第1期2014卷

作者： Zhou, Mu Tian, Zengshan Xu, Kunjie Wu, Haibo Pu, Qiaolin Yu, Xiang Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Graduate Telecommunications and Networking Program University of Pittsburgh Pittsburgh PA 15260 United States China Internet Research Lab Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China

Path tracking in wireless and mobile environments is a fundamental technology for ubiquitous location-based services (LBSs). In particular, it is very challenging to develop highly accurate and cost-efficient tracking systems applied to the anonymous areas where the floor plans are not available for security and privacy reasons. This paper proposes a novel path tracking approach for large Wi-Fi areas based on the time-stamped unlabeled mobility map which is constructed from Smith-Waterman received signal strength (RSS) measurement matching. Instead of conventional location fingerprinting, we construct mobility map with the technique of dimension reduction from the raw measurement space into a low-dimensional embedded manifold. The feasibility of our proposed approach is verified by the real-world experiments in the HKUST campus Wi-Fi networks, sMobileNet. The experimental results prove that our approach is adaptive and capable of achieving an adequate precision level in path tracking. © 2014 Mu Zhou et al.

关键词： Wi-Fi

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Floor plan construction via anonymous Wi-Fi signal fragment matching

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ICIC Express Letters, Part B: Applications 2014年第4期5卷 981-987页

作者： Zhou, Mu Tian, Zengshan Xu, Kunjie Tang, Xiaomou Wang, Zhuoran Chongqing Key Lab of Mobile Communications Technology School of Communication and Information Engineering Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Nan'an Dist. Chongqing 400065 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Graduate Telecommunications and Networking Program University of Pittsburgh PA 15260 United States

Floor plan construction is the basis for fingerprint-based localization and people's activity learning, especially in the environments where the geometrical map is not available or does not exist. To this end, we propose a new received signal strength (RSS) fragment matching approach to identify the location points (LPs) in floor plan as the overlapped pieces between the fragments. Then, by means of the Smith-Waterman alignment, each LP can be recognized as a winning path in scoring space. Finally, we use the Allen's interval algebra to assemble the time-stamped LPs into a floor plan in target area. The experiments with the actual Wi-Fi RSS measurements recorded in HKUST campus demonstrate that our proposed approach performs better than the conventional K-means clustering and spectral clustering in floor plan construction. © 2014 ICIC International.

关键词： Algebra

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