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Forecasting inflation in post-oil boom years: A case for regime switches?

Journal of Economics and Finance 2018年第2期42卷 369-385页

作者： Ahmadov, Vugar Huseynov, Salman Adigozalov, Shaig Mammadov, Fuad Rahimov, Vugar Center for Research and Development Central Bank of Azerbaijan Baku Azerbaijan Centre for Research and Development Central Bank of Azerbaijan and Institute of Control Systems National Academy of Sciences Baku Azerbaijan

In this study, we investigate the relative performance of various non-linear models against that of an autoregressive model in forecasting future inflation. We find that non-linear models have trivial forecast superiority over the univariate autoregressive model in terms of central forecast accuracy. They also perform poorly when their forecasts are measured against those of a VAR model. In addition, we also show that non-linear models cannot beat the random walk in terms of central forecast accuracy, which is in line with the previous literature on Azerbaijan during the post-oil boom years. However, we also demonstrate that non-linear models still have clear forecast advantages over both linear and random walk models in predicting forecast density. © 2017, Springer Science+Business Media, LLC.

关键词： Bayesian methods Forecasting Inflation Regime switching models

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A Guide for Human Walking Model and control — Insights from Mechanical Property Analysis of Human Walking

A Guide for Human Walking Model and Control — Insights from...

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Chinese control and Decision Conference, CCDC

作者： Guangrong Chen Bowen Hou Sheng Guo Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing P.R. China Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention Beijing P.R. China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P.R. China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper aims to establish the simplest human walking model and provide a guide for controlling biped robots. Firstly, a human motion capture system was utilized to sample the motion data of human walking and a representative subject was selected. Secondly, one of the simplest human walking model was set up. The mechanical property analysis of human walking was done based on the established walking model via fitting. Thirdly, the feasibility and effectiveness of proposed walking model were validated by numerical simulations. Finally, the intrinsical relationships among human walking, biped walking model and control were discussed to provide a guide and insight for controlling biped robots.

关键词： Legged locomotion Numerical models Force Analytical models Numerical simulation

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Burdens of type 2 diabetes and cardiovascular disease attributable to sugar-sweetened beverages in 184 countries (06 Jan, 10.1038/s41591-024-03345-4, 2025)

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NATURE MEDICINE 2025年第2期31卷 696-696页

作者： Lara-Castor, Laura O'Hearn, Meghan Cudhea, Frederick Miller, Victoria Shi, Peilin Zhang, Jianyi Sharib, Julia R. Cash, Sean B. Barquera, Simon Micha, Renata Mozaffarian, Dariush Food Is Medicine Institute Friedman School of Nutrition Science and Policy Tufts University Boston MA USA Institute of Health Metrics and Evaluation University of Washington Seattle WA USA Food Systems for the Future Institute Chicago IL USA Department of Medicine McMaster University Hamilton Ontario Canada Population Health Research Institute Hamilton Ontario Canada University of Thessaly Volos Greece Tufts University School of Medicine Boston MA USA Department of Medicine Tufts Medical Center Boston MA USA Center for Surgery and Public Health Brigham and Women’s Hospital Boston MA USA Friedman School of Nutrition Science and Policy Tufts University Boston MA USA Research Center on Nutrition and Health National Institute of Public Health Cuernavaca Mexico National Institute of Public Health (INSP) Cuernavaca Mexico Academy of Athens Athens Greece Acibadem University Istanbul Turkey Addis Ababa University Addis Ababa Ethiopia Aga Khan Foundation Geneva Switzerland All India Institute of Medical Sciences New Delhi India American University of Beirut Beirut Lebanon Amrita School of Dentistry Eranakulum India Andalas University Padang Indonesia Arab Center for Nutrition Manama Bahrain Ardabil University of Medical Sciences Ardabil Iran Bacha Khan University Charsadda Pakistan Biodiversity International Nairobi Kenya Brighton and Sussex Medical School Brighton UK Center for Studies of Sensory Impairment Aging and Metabolism (CeSSIAM) Guatemala City Guatemala Centre For Media Studies New Delhi India Centro de Atencion Nutricional Antimano (CANIA) Miami FL USA Centro de Estudios sobre Nutrición Infantil (CESNI) Buenos Aires Argentina Child Growth and Development Research Center Isfahan University of Medical Sciences Isfahan Iran College of Applied Sciences Department of Food Science and Applied Nutrition Addis Ababa Science and Technology University Addis Ababa Ethiopia Consultative Group on International Agricultural Research (CGIAR) M

The consumption of sugar-sweetened beverages (SSBs) is associated with type 2 diabetes (T2D) and cardiovascular diseases (CVD). However, an updated and comprehensive assessment of the global burden attributable to SSBs remains scarce. Here we estimated SSB-attributable T2D and CVD burdens across 184 countries in 1990 and 2020 globally, regionally and nationally, incorporating data from the Global Dietary Database, jointly stratified by age, sex, educational attainment and urbanicity. In 2020, 2.2 million (95% uncertainty interval 2.0–2.3) new T2D cases and 1.2 million (95% uncertainty interval 1.1–1.3) new CVD cases were attributable to SSBs worldwide, representing 9.8% and 3.1%, respectively, of all incident cases. Globally, proportional SSB-attributable burdens were higher among men versus women, younger versus older adults, higher- versus lower-educated adults, and adults in urban versus rural areas. By world region, the highest SSB-attributable percentage burdens were in Latin America and the Caribbean (T2D: 24.4%; CVD: 11.3%) and sub-Saharan Africa (T2D: 21.5%; CVD: 10.5%). From 1990 to 2020, the largest proportional increases in SSB-attributable incident T2D and CVD cases were in sub-Saharan Africa (+8.8% and +4.4%, respectively). Our study highlights the countries and subpopulations most affected by cardiometabolic disease associated with SSB consumption, assisting in shaping effective policies and interventions to reduce these burdens globally.

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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IEEE International Conference on Networking, Sensing and control

作者： Shichao Chen Qijie Li Hua Zhang Fenghua Zhu Gang Xiong Ying Tang Macau University of Science and Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Macau China Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Engineering Research Center of Intelligent Systems and Technology Institute of Automation and Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Glassboro NJ USA Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT.

关键词： Energy consumption Processor scheduling Computer architecture Internet of Things Optimization Monitoring Edge computing

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Adaptive Dynamic Programming Method for Optimal Battery Management of Battery Electric Vehicle

Adaptive Dynamic Programming Method for Optimal Battery Mana...

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Data Driven control and Learning systems (DDCLS)

作者： Jiguang Xue Chunsheng Yan Dan Wang Jun Wang Jun Wu Zehua Liao Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd Shenyang China LiaoNing Electric Power Development Stock Co. LTD Shenyang China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

One of the main influencing factors of battery electric vehicle (BEV) application is the high-cost of the battery. We consider to apply the battery of BEV to smart residential environments when the BEV is idle, so that we can lower the utility cost. Therefore, an adaptive dynamic programming (ADP) method is designed to solve the optimal battery management, which avoids the dimension disaster of the complex nonlinear BEV system. First, the operation modes of the battery are analyzed, and the problem statement is carried out. Then, the corresponding self-learning optimization algorithm is developed based on ADP. Finally, numerical results by experiment simulations are used to verify the ADP algorithm.

关键词： Batteries Optimal control Dynamic programming Battery management systems Power grids Discharges (electric) Adaptation models

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Conception of Ontology for Computer Methods of Paintings Analysis

Conception of Ontology for Computer Methods of Paintings Ana...

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2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019

作者： Beloozerov, Viktor Murashov, Dmitrij Trusova, Yuliya Berezin, Aleksej Ivanova, Ekaterina All-Russia Institute for Scientific and Technical Information Department for Classification Systems Development Moscow Russia Institute of Educational Informatics Federal Research Center 'Information and Control' Moscow Russia A. A. Dorodnicyn Computing Center Federal Research Center 'Information and Control' Moscow Russia State Historical Muzeum Restoration Department Moscow Russia Ilya Glazuvov Russian Academy of Painting Sculpture and Architecture Department of Painting Restoration Moscow Russia

ISBN: (纸本)9781728144016

This paper describes the procedure of developing the 'OntArt' ontology for the subject area 'Attribution and restoration of works of easel oil painting'. Special attention is paid to the domain of computer methods application. The basis of the work is a previously developed thesaurus of this domain. The conceptual shell of the original thesaurus descriptors is the standard CIDOC CRM framework, which binds domain concepts to abstract philosophical categories. In order to connect particular concepts with abstract categories, it is proposed to include 21 intermediate-level conceptual classes in the CIDOC CRM structure. Thus, we have built a formalized structure of concepts for the area of attribution and restoration of painting, ranging from the highest philosophical abstractions to specialized methods of computer analysis of picture images. This structure includes about 1300 terms in Russian. The English part has about 500 terms and is replenished as the interlanguage equivalence of the terms is agreed. In the part of vocabulary of computer methods, we have a full bilingual ontology with one-To-one correspondence of the Russian and English versions. The current version of the ontology is implemented as a TiddlyWiki software module and is publicly available. © 2019 IEEE.

关键词： Ontology

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Non-Periodic Lower-Limb Motion Recognition with Noncontact Capacitive Sensing

Non-Periodic Lower-Limb Motion Recognition with Noncontact C...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Enhao Zheng Jinchen Zeng Dongfang Xu Qining Wang Hong Qiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No. 95 of Zhongguancun East Road Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing 30 Xueyuan Road Beijing Robotics Research Group College of Engineering Peking University Beijing China

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Noncontact captive sensing is a new sensing strategy that we proposed previously to compensate for the limitations of existing surface electromyography studies for exoskeleton control. It has been validated on locomotion mode recognition and gait phase estimation. However, our previous studies addressed the tasks of periodic ambulation based on machine learning algorithms. The performances of the capacitive sensing on non-periodical lower-limb motion recognition have never been evaluated. In this preliminary study, we designed a motion recognition method by fusing the capacitive sensing and the inertial sensors. The recognition algorithm was designed based on the combined logics, which freed the system from burdensome training procedures in the recognition tasks. The method was validated on three healthy subjects in performing 6 lower-limb motions and the transitions between them (10 in total). The capacitance-inertial fusion method produced an average precision/recall of >0.92 in static motions and >0.86 with transitions. The most prominent improvement of using the capacitance signals is that it increases the time-response ability during the motion transitions. Compared with purely using inertial sensors, the sensor fusion method reduced more than 100-ms latency on average. The pilot study extends the scope of the new sensing method in human motion recognition. Future efforts will be paid in this direction to get more promising results.

关键词： Robot sensing systems Capacitance Electrodes Task analysis Legged locomotion Exoskeletons

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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Experimental investigation of solar PVT collector with the dryer on mass and temperature of dried red chili with Machine Learning Models

Results in Engineering

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Results in Engineering 2025年

作者： Miroslav Mahda K. Rajathi Muniyandy Elangovan Sellamuthu Prabhu kumar Amit Verma Department of Control Systems and Instrumentation Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu-600 062 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Applied Science Research Centre Applied Science Private University Amman Jordan Department of Mechanical Engineering Presidency University Bengaluru 560064 India University Centre for Research and Development Chandigarh University Gharuan Mohali Punjab India

The growing demand for efficient drying methods in agricultural processes has motivated researchers to explore hybrid systems using photovoltaic and thermal systems. One challenge faced in this research was the limited amount of drying days and dealing with one crop (red chilies), which could limit the potential application for other crops. The kinetics of drying red chilies with a photovoltaic thermal (PVT) system were in combination with a dryer, and the objective was to enhance the drying process of red chilies under various environmental conditions of surface glazing temperature, solar radiation, the temperature of the outflow fluid, and ambient temperature. Three drying techniques open sun drying, forced convection drying, and natural convection drying with varying flow velocities served as the foundation for the research. Using this controlled case-study method, 3 kg of red chilies were dried under standard sun radiation from an ambient temperature of 31°C to a high of 58°C. The moisture content reduction process was measured by starting with 79 % moisture content by weight after drying the red chilies for a total of six days conducting the experiments between 9am and 4pm during Mars, April, and May 2023, each time on clear days with direct sunlight. The drying process was stop at 11 % moisture content after 6-day testing round of red chilies drying. Machine learning (ML) models, namely the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree (DT), were used to forecast the temperature and mass dryness *** RBF model showed the best performance with 0.98, 0.95, and 0.92 for temperature dryness, above MLP and DM. The research finds that the RBF model had the highest capacity to forecast drying efficiency and that forced convection drying is more effective than open solar drying.

关键词： red chilies,electrical and thermal efficiency Photovoltaic Thermal solar dryer solar radiation temperature Solar energy

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