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Difficulty adjustable and scalable constrained multiobjective test problem toolkit

Evolutionary Computation 2019年第3期28卷 339-378页

作者： Fan, Zhun Li, Wenji Cai, Xinye Li, Hui Wei, Caimin Zhang, Qingfu Deb, Kalyanmoy Goodman, Erik Department of Electronic Engineering Shantou University Guangdong515063 China Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China School of Mathematics and Statistics Xi’an Jiaotong University Shan’xi710049 China Department of Computer Science City University of Hong Kong Hong Kong BEACON Center for the Study of Evolution in Action Michigan State University East LansingMI United States

Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, butmost of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrainedmany-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving conv

关键词： Hardness

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High Information Density and Low Coverage Data Storage in DNA with Efficient Channel Coding Schemes

arXiv

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arXiv 2024年

作者： Ding, Yi He, Xuan Nguyen, Tuan Thanh Song, Wentu Yakhini, Zohar Yaakobi, Eitan Pan, Linqiang Tang, Xiaohu Cai, Kui Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China Cluster Singapore University of Technology and Design 487372 Singapore Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel School of Computer Science RUNI Herzliya4615200 Israel Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

DNA-based data storage has been attracting significant attention due to its extremely high data storage density, low power consumption, and long duration compared to conventional data storage media. Despite the recent advancements in DNA data storage technology, significant challenges remain. In particular, various types of errors can occur during the processes of DNA synthesis, storage, and sequencing, including substitution errors, insertion errors, and deletion errors. Furthermore, the entire oligo may be lost. In this work, we report a DNA-based data storage architecture that incorporates efficient channel coding schemes, including different types of error-correcting codes (ECCs) and constrained codes, for both the inner coding and outer coding for the DNA data storage channel. We also carried out large scale experiments to validate our proposed DNA-based data storage architecture. Specifically, 1.61 and 1.69 MB data were encoded into 30,000 oligos each, with information densities of 1.731 and 1.815, respectively. It has been found that the stored information can be fully recovered without any error at average coverages of 4.5 and 6.0, respectively. This experiment achieved the highest net information density and lowest coverage among existing DNA-based data storage experiments (with standard DNA), with data recovery rates and coverage approaching theoretical optima. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

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A New Calibration of Soil Roughness Effects in the SMOS-IC Algorithm for Soil Moisture and VOD Retrievals

A New Calibration of Soil Roughness Effects in the SMOS-IC A...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Konkathi Preethi Xiaojun Li Roberto Fernandez-Moran Xiangzhuo Liu Zanpin Xing F. Frappart Maria Piles Karthikeyan Lanka Jean-Pierre Wigneron Centre of Studies in Resources Engineering Indian Institute of Technology Bombay India INRAE UMR1391 ISPA Villenave d'Ornon France Image Processing Lab (IPL) University of Valencia Valencia Spain Cryosphere Research Station on the Qinghai-Tibet Plateau State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and Resource Chinese Academy of Sciences Lanzhou China IDP in Climate Studies Indian Institute of Technology Bombay India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Soil Moisture Ocean Salinity (SMOS) mission was the first L-band radiometer launched in 2010 and is operational for retrieving global scale soil moisture and Vegetation Optical Depth (VOD). SMOS-INRA-CESBIO (SMOS-IC) version-2 is the latest retrieval algorithm for SMOS radiometers that outperforms existing SMOS retrieval algorithms. Research is underway to enhance the SMOS-IC product by improving surface roughness information that influences soil moisture and VOD retrievals. In the present study, we developed a new global parameterization of soil roughness using SMOS-IC retrievals. For this purpose, we retrieved the soil moisture and surface roughness (through the Hr parameter) values over bare soils using the SMOS-IC algorithm. A Random Forest (RF) model was trained with soil textural and terrain properties as inputs (explanatory variables) to model Hr over bare soils. Later, we extrapolated the Hr values obtained over bare soils to a global scale using the RF model. The newly calibrated Hr values were further used in the SMOS-IC algorithm for soil moisture and VOD retrievals (SMOS-IC v2.1). The SMOS-IC v2.1, the soil moisture product, is wetter than the original SMOS-IC v2 product and has improved performance compared to in situ ISMN soil moisture and modeled ECMWF soil moisture data sets. Regarding VOD, the SMOS-IC v2.1 VOD product showed improved spatial correlation with the reference aboveground biomass product. In addition, SMOS-IC v2.1 VOD product showed improved temporal correlation with MODIS NDVI over low-to-moderate vegetated regions.

关键词： Radio frequency Sea surface Correlation Soil moisture Vegetation mapping Surface roughness Rough surfaces

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Joint DBF and SAO Parallel Filtering Based on Multithread Load Balancing

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hao Ma Dong Hu Yi Li Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme.

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Scene matching based on non-linear pre-processing on reference image and sensed image

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Journal of Systems Engineering and Electronics 2005年第2期000卷

作者： ZHONG Sheng Zhang Tianxu Sang Nong State Key Lab for Image Processing & Intelligent Control， Institute for Pattern Recognition and Artificial Intelligence，Huazhong University of Science and Technology， Wuhan 430074， P. R. China

To solve the heterogeneous image scene matching problem, a non-linear pre-processing method for the original images before intensity-based correlation is proposed. The result shows that the proper matching probability... 详细信息

关键词： intensity-based correlation heterogeneous image scene matching

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Scale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Ting Zhang Dong Hu Jing Zhang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.

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Utility-Oriented Underwater image Quality Assessment Based on Transfer Learning

arXiv

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater image Utility Database (UIUD) and a learning-based Underwater image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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Discrete, recurrent, and scalable patterns in human judgement underlie affective picture ratings

arXiv

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arXiv 2022年

作者： Azcona, Emanuel A. Kim, Byoung-Woo Vike, Nicole L. Bari, Sumra Lalvani, Shamal Stefanopoulos, Leandros Woodward, Sean Block, Martin Katsaggelos, Aggelos K. Breiter, Hans C. Image and Video Processing Lab Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States Department of Psychiatry and Behavioral Sciences Northwestern University ChicagoIL United States Integrated Marketing Communications Northwestern University EvanstonIL United States Department of Computer Science Northwestern University EvanstonIL United States Department of Radiology Northwestern University ChicagoIL United States Laboratory of Neuroimaging and Genetics Department of Psychiatry Massachusetts General Hospital Harvard School of Medicine BostonMA United States

Operant keypress tasks, where each action has a consequence, have been analogized to the construct of "wanting" and produce lawful relationships in humans that quantify preferences for approach and avoidance behavior. It is unknown if rating tasks without an operant framework, which can be analogized to "liking", show similar lawful relationships. We studied three independent cohorts of participants (N = 501, 506, and 4,019 participants) collected by two distinct organizations, using the same 7-point Likert scale to rate negative to positive preferences for pictures from the International Affective Picture Set. Picture ratings without an operant framework produced similar value functions, limit functions, and tradeoff functions to those reported in the literature for operant keypress tasks, all with Rn goodness of fits above 0.75. These value, limit, and trade-off functions were discrete in their mathematical formulation, recurrent across all three independent cohorts, and demonstrated scaling between individual and group curves. In all three experiments, the computation of loss aversion showed 95% confidence intervals below the value of 2, arguing against a strong overweighting of losses relative to gains, as has previously been reported for keypress tasks or games of chance with calibrated uncertainty. Graphed features from the three cohorts were similar and argue that preference assessments meet three of four criteria for lawfulness, providing a simple, short, and low-cost method for the quantitative assessment of preference without forced choice decisions, games of chance, or operant keypressing. This approach can easily be implemented on any digital device with a screen (e.g., cellphones). © 2022, CC BY.

关键词： Big data

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3D face registration based on principal axis analysis and labeled regions orientation

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Journal of Systems Engineering and Electronics 2009年第6期000卷

作者： Guo Zhe Zhang Yanning Lin Zenggang Liu Yantong Shaanxi Key Lab. of Speech& Image Information Processing School of Computer ScienceChang'an Campus of Northwestern Polytechnical Univ. Xi'an 710129 P. R. China

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is *** pre-registration is achieved by transforming the multi-pose models to the standard frontal model\'s reference frame using the principal axis analysis *** significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution *** regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final *** results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

关键词： local orientation registration principal axis analysis label regions orientation iterative closest point

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Non-sparse Infinite-Kernel Learning for Automated Identification of Alzheimer's Disease Using PET Imaging

Non-sparse Infinite-Kernel Learning for Automated Identifica...

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International Conference on Control, Automation, Robotics & Vision

作者： Yong Xia Shen Lu Wei Wei David Dagan Feng Yanning Zhang Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) School of Computer Science Northwestern Polytechnical University Xi'an 710072 China Biomedical and Multimedia Information Technology (BMIT) Research Group School of Information Technologies The University of Sydney NSW2006 Australia

ISBN: (纸本)9781479952007

Multi-kernel learning machine (MKLM) has recently been introduced to the research of computer-aided dementia identification and pathology progress tracking. Despite its good performance especially in case of using heterogeneous data, such learning schema and its variants usually utilize a L-1 norm constraint that promotes sparse solutions, which may cause loss of potentially important information. In this paper, we propose the non-sparse infinite-kernel learning machine (NS-IKLM) for automated identification of Alzheimer cases from normal controls. In our approach, a modified constraint is utilized to promotes non-sparse solutions and kernel parameters are automatically tuned during the learning process. The proposed algorithm has been evaluated on a set of FDG-PET images selected from the Alzheimer's disease neuroimaing initiative (ADNI) cohort. Our results demonstrate that the proposed non-sparse NS-IKLM is able to achieve satisfying dementia identification at a relatively low computational cost.

关键词： Infinite kernel learning dementia FDG-PET ADNI

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