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SSRN 2023年

作者： Pan, Li Li, Jiarong Huang, Jingsi An, Qi Lin, Jin Mujeeb, Asad Xu, Yanhui Li, Gengyin Zhou, Ming Wang, Jianxiao School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China Department of Electrical Engineering Tsinghua University Beijing100084 China Harvard John A. Paulson School of Engineering and Applied Sciences CambridgeMA02138 United States Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China PKU-Changsha Institute for Computing and Digital Economy Changsha410000 China

The increasing demand for nitrogen fertilizer and chemical raw materials has provided an opportunity for the ammonia (NH3) industry. Although there are many excellent reviews proposing operation control strategies and capacity configuration strategies of renewable-to-ammonia (RE2A) systems, surprisingly little attention has been devoted to topics of public interest. Therefore, this paper proposes a technoeconomic model to research the optimal capacity configuration and quantify the levelized cost of ammonia (LCOA) for RE2A. A cost model of coal-to-ammonia (C2A) is established as a benchmark to evaluate the economic feasibility of RE2A. A case study is adopted to verify the validity of the proposed approach. It is shown that the monthly NH3 output is 6-10% of the annual output, which satisfies actual industrial production. The LCOA of RE2A is 507$/t, with investments in wind turbines and electrolysers accounting for 57% and 20%, respectively, which is lower than the NH3 market price (605-650$/t) in China at present. The LCOA of RE2A will equal the LCOA of C2A with an extra carbon tax of 47.5$/t, which confirms the economic and environmental advantages of RE2A in the long run. The sensitivity analysis of RE2A in comparison with C2A provides valuable guidelines for the application of RE2A. © 2023, The Authors. All rights reserved.

关键词： Ammonia

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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An accuracy comparison between the time-series and the computational intelligence models on the Taiwan-central America free trade agreements

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ICIC Express Letters, Part B: Applications 2016年第9期7卷 1925-1932页

作者： Tsai, Pei-Wei Yang, Li-Hui Zhang, Jing Xue, Xingsi Chen, Jung-Fang Chang, Jui-Fang Pan, Jeng-Shyang College of Information Science and Engineering Fujian University of Technology University Town No. 3 Xueyan Road Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology University Town No. 3 Xueyan Road Minhou Fuzhou350118 China Department of International Business National Kaohsiung University of Applied Sciences No. 415 Chien-Kong Road Kaohsiung City80778 Taiwan Department of Business Administration National Kaohsiung University of Applied Sciences No. 415 Chien-Kong Road Kaohsiung City80778 Taiwan

Starting from the mid-20th century, Taiwanese economy is extremely exportoriented due to the lack of natural resources. To ensure the growing of the economy, Taiwan government keeps actively looking for opportunities for participation in the world economic system to avoid the slowdown of multinational liberalization. As one example, Taiwan vigorously participates in FTAs with countries of Central America. International economic conditions are significantly important to international trade and the export/import volume will be affected by the foreign exchange rate. If the impact level of an incident or an event occurrence is analyzed along with the foreign exchange rate forecasting, there is a chance to maximize the trade surplus, thus boosting the economic growth. Based on the event study methodology, the selected agreements include four Taiwan signed FTAs with countries in Central America. The observation period setting is 70 days of pre-event period and 70 days of post-event period. In this paper, we use two commonly used time-series models including the GARCH and the EGARCH and a Computational Intelligence (CI) model established by the Interactive Artificial Bee Colony (IABC) method for finding the impact on the foreign exchange rate forecasting. The Mean Absolute Percentage Error (MAPE) is adopted to compare the accuracy of exchange rate prediction. The experimental results indicate that the IABC based foreign exchange rate forecasting model presents higher forecasting accuracy than the conventional time-series models and is able to resist the impact caused by the FTAs. © 2016 ICIC International.

关键词： International trade

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Artificial Neural Network-Based Direct Power Control to Enhance the Performance of a Pmsg-Wind Energy Conversion System Under Real Wind Speed and Parameter Uncertainties: An Experimental Validation

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SSRN 2023年

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Science and Technology Kraków30-059 Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered significant attention, owing to its straightforward and highly adaptable algorithm. It has been adopted in many applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2023, The Authors. All rights reserved.

关键词： MATLAB

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A new certificate-based aggregate signature scheme for wireless sensor networks

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Journal of Information Hiding and Multimedia Signal Processing 2018年第5期9卷 1264-1280页

作者： Chen, Jian-Neng Zou, Fu-Min Wu, Tsu-Yang Zhou, Yu-Ping College of computer Minnan Normal University ZhangzhouFujian363000 China Beidou Navigation and Smart Traffic Innovation Center of Fujian Province FuzhouFujian350008 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology FuzhouFujian350008 China College of Computer Science and Engineering Shandong University of Science and Technology QingdaoShandong266590 China Key Laboratory of Applied Mathematics Putian University Fujian Province University PutianFujian351100 China

data transmission is one of the important basic technologies in wireless sensor networks (WSN). Due to the limited energy, storage capacity, computing power and communication ability of sensor nodes, the efficiency of data transmission is greatly limited. Certificate-based aggregate signature scheme provides an efficient way to combine numerous signatures into one short signature. In this paper, we propose a certificate-based aggregate signature scheme in WSN. In the random oracle model and under the computational Diffie-Hellman (CDH) Problem and bilinear Diffie-Hellman (BDH), we demonstrate that our scheme is provably secure against forgery attack. The performance analysis demonstrates that our scheme provides an efficient way for data transmission and is suitable in WSN. © 2018, Ubiquitous International. All rights reserved.

关键词： data transfer

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Hierarchical Attention Feature Fusion and Refinement Network for Point Cloud Upsampling

Hierarchical Attention Feature Fusion and Refinement Network...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yaori Zhang Shujin Lin Fan Zhou Ruomei Wang National Engineering Research Center of Digital Life School of Computer Science and Engineering Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China School of Communication and Design Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

This paper presents a novel hierarchical attention feature fusion and refinement network designed to address challenges in existing deep learning based point cloud upsampling methods. The network combines self-attention layers with a multi-level feature extraction architecture, effectively integrating local and global features, thereby enhancing the robustness and uniformity of the point cloud. Furthermore, a spatial refinement module is employed to predict the offset between the generated coarse dense point clouds and real point clouds, thereby enhancing consistency with the ground truth. Concurrently, a filter function is applied in the loss function to handle outliers of generated point clouds. Extensive experimental results across multiple datasets indicate that our method outperforms existing approaches.

关键词： Point cloud compression Deep learning Feature extraction Robustness Encoding

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A Solution-Based Tabu Search Algorithm for the Resource-Constrained Project Scheduling Problem with Step Deterioration

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Dai, Huafeng Li, Lei Mao, Run Liu, Xingyue Zhou, Kun School of Mechanical Engineering Chengdu University Chengdu610105 China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Chengdu610031 China CNPC National Engineering Research Center for Oil & Gas Drilling Equipment Co. Ltd. Chengdu610052 China School of Information Science and Engineering Chengdu University Chengdu610105 China Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm. © 2023 Huafeng Dai et al.

关键词： Deterioration

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Neural multi-objective combinatorial optimization with diversity enhancement 23

Neural multi-objective combinatorial optimization with diver...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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Evaluation of TRMM 3B43 V7 precipitation data in varied Moroccan climatic and topographic zones

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Mediterranean Geoscience Reviews 2024年第2期6卷 159-175页

作者： Aqnouy, Mourad Ommane, Younes Ouallali, Abdessalam Gourfi, Abdelali Ayele, Gebiaw T. El Yousfi, Yassine Bouizrou, Ismail Stitou El Messari, Jamal Eddine Zettam, Amin Melesse, Assefa M. Ndehedehe, Christopher E. Abioui, Mohamed Briak, Hamza Applied Geology Research Laboratory AGRSRT Department of Geosciences Faculty of Sciences and Techniques Moulay Ismail University of Meknes Errachidia Morocco Modelling Simulation and Data Analysis Mohammed VI Polytechnic University (UM6P) Ben Guerir Morocco GPE Laboratory Faculty of Sciences and Techniques Hassan II University of Mohammedia Mohammedia Morocco ESO UMR 6590 UNIV Angers CNRS SFR Confluences UFR LLSH Angers France Australian Rivers Institute and School of Engineering and Built Environment Griffith University Nathan Australia Research Team: Water and Environment Management Laboratory of Applied Sciences (LSA) National School of Applied Sciences Al Hoceima Abdelmalek Essaadi University Tétouan Morocco Functional Ecology and Environmental Engineering Laboratory Faculty of Sciences and Techniques Sidi Mohamed Ben Abdellah University Fez Morocco Department of Agriculture Food Environment and Forestry (DAGRI) University of Florence Florence Italy Department of Geology Faculty of Sciences Abdelmalek Essaadi University Tétouan Morocco LECGEN SNVSTU Abou Bekr Belkaid University (UABB) Tlemcen Algeria Department of Earth and Environment Florida International University Miami FL United States School of Environment and Science Griffith University Nathan QLD Australia Geosciences Environment and Geomatics Laboratory (GEG) Department of Earth Sciences Faculty of Sciences Ibnou Zohr University Agadir Morocco MARE-Marine and Environmental Sciences Centre-Sedimentary Geology Group Department of Earth Sciences Faculty of Sciences and Technology University of Coimbra Coimbra Portugal Laboratory for Sustainable Innovation and Applied Research Universiapolis—International University of Agadir Agadir Morocco Center of Excellence for Soil and Fertilizer Research in Africa (CESFRA) AgroBioSciences (AgBS) Mohammed VI Polytechnic University (UM6P) Ben Guerir Morocco

Hydrologists in developing countries face a major challenge due to the lack of ground data. This study evaluates the performance of the monthly Tropical Rainfall Measurement Mission (TRMM) algorithm 3B43 V7 using 172 ground rainfall measurement stations (GRMS) over Morocco (1998–2018). The performance of the TRMM 3B43 V7 was assessed using four well-known metrics (KGE, R², PBIAS, and RSR). The evaluation was undertaken based on (1) a point-to-pixel scale, (2) sub-regions of Morocco, (3) climatic zones, and (4) different ranges of altitudes. Results indicate that the TRMM 3B43 V7 provides satisfactory attribution in most regions of Morocco, with an average R² higher than 0.54, except in the south areas, which have the lowest R² between 0.29 and 0.41. The best R² of 0.95 was observed in the Tangier-Tetouan-Al Hoceima region. The other performance criteria (KGE: 0.93, RSR: 0.21, PBIAS: − 5.5) indicate that TRMM 3B43 V7 is consistent with in situ data but slightly underestimated. At the point-to-pixel scale, KGE and R² show that 61% and 52% of stations present satisfactory to good results, respectively. The PBIAS shows a large overestimation over most parts of Morocco, particularly the southern part. TRMM 3B43V7 performs better in the Mediterranean climate than in other climatic zones. TRMM 3B43 V7 harmonises GRMS slightly in low elevation zones, while average PBIAS show a global overestimation, increasing in high and low elevations. The results of this study confirm that TRMM 3B43 V7 provides a vital baseline tool to assist Moroccan Hydraulic Basin agencies. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： GRMS Morocco Rainfall Topography TRMM 3B43 V7

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Gsgm: A Group-Sequence-Aware Graph Model for Personalized News Recommender System

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SSRN 2024年

作者： Lv, Pengtao Zhang, Qinghui Guan, Zhenhan Deveci, Muhammet Shi, Lei Zhong, Kaiyang Wang, Jingping Xu, Heliang Zhang, Yana Ministry of Education Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Grain Big Data Analysis and Application Engineering Research Center Henan University of Technology Henan Zhengzhou450001 China The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul4940 Turkey State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages Minzu University of China Beijing100081 China Southwestern University of Finance and Economics China

News recommender system is an important method of helping readers identify interesting stories. Although human behaviors exhibit sequential patterns, most current studies on news recommendations ignore the influence of reading sequences. Leveraging sequential patterns in news recommendation is, however, very challenging, as the capture of reading behavior sequence at the individual level may be impossible due to severity of sparsity in the field of news. To address this challenge, we proposed a group-sequence-aware graph model (GSGM) to learn representative reading sequences of groups instead of individuals, where groups are viewed as latent variables and automatically learnt from users' reading behaviours. Besides, when readers select different groups, they are likely to have distinct reading preferences. This finding is used to learn group-dependent personal reading preferences in GSGM. A wide range of experiments are conducted to verify the efficiency and effectiveness of the GSGM on real datasets. Experimental results demonstrate that GSGM outperforms the state-of-the-art competing methods. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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