Citation: Yuyang Zhou, Randa Herzallah. Probabilistic message passing control and FPD based decentralised control for stochastic complex systems[J]. AIMS Electronics and Electrical Engineering, 2020, 4(2): 216-233. doi: 10.3934/ElectrEng.2020.2.216
[1] | Theodor Friedrich, Amir Kassam . Food security as a function of Sustainable Intensification of Crop Production. AIMS Agriculture and Food, 2016, 1(2): 227-238. doi: 10.3934/agrfood.2016.2.227 |
[2] | Boris Boincean, Amir Kassam, Gottlieb Basch, Don Reicosky, Emilio Gonzalez, Tony Reynolds, Marina Ilusca, Marin Cebotari, Grigore Rusnac, Vadim Cuzeac, Lidia Bulat, Dorian Pasat, Stanislav Stadnic, Sergiu Gavrilas, Ion Boaghii . Towards Conservation Agriculture systems in Moldova. AIMS Agriculture and Food, 2016, 1(4): 369-386. doi: 10.3934/agrfood.2016.4.369 |
[3] | Mena Ritota, Pamela Manzi . Edible mushrooms: Functional foods or functional ingredients? A focus on Pleurotus spp.. AIMS Agriculture and Food, 2023, 8(2): 391-439. doi: 10.3934/agrfood.2023022 |
[4] | Maurizio Canavari, Federico Gori, Selene Righi, Elena Viganò . Factors fostering and hindering farmers' intention to adopt organic agriculture in the Pesaro-Urbino province (Italy). AIMS Agriculture and Food, 2022, 7(1): 108-129. doi: 10.3934/agrfood.2022008 |
[5] | João Paulo Curto, Pedro Dinis Gaspar . Traceability in food supply chains: Review and SME focused analysis-Part 1. AIMS Agriculture and Food, 2021, 6(2): 679-707. doi: 10.3934/agrfood.2021041 |
[6] | Germaine Ibro, Ibro Madougou Abdoulaye, Gry Synnevåg, Jens B. Aune . Food security and child malnutrition in the regions of Maradi, Tahoua and Tillabéri in Niger: The status, the causes, and transformative change. AIMS Agriculture and Food, 2022, 7(3): 704-720. doi: 10.3934/agrfood.2022043 |
[7] | Isaac Busayo Oluwatayo . Towards assuring food security in South Africa: Smallholder farmers as drivers. AIMS Agriculture and Food, 2019, 4(2): 485-500. doi: 10.3934/agrfood.2019.2.485 |
[8] | Mohamed-Yousif Ibrahim Mohamed . Campylobacteriosis in North Africa. AIMS Agriculture and Food, 2024, 9(3): 801-821. doi: 10.3934/agrfood.2024043 |
[9] | Alexandros Tsoupras, Eirini Panagopoulou, George Z. Kyzas . Olive pomace bioactives for functional foods and cosmetics. AIMS Agriculture and Food, 2024, 9(3): 743-766. doi: 10.3934/agrfood.2024040 |
[10] | M. Laura Donnet, Iraís Dámaris López Becerril, J. Roy Black, Jon Hellin . Productivity differences and food security: a metafrontier analysis of rain-fed maize farmers in MasAgro in Mexico. AIMS Agriculture and Food, 2017, 2(2): 129-148. doi: 10.3934/agrfood.2017.2.129 |
Many scientific and technological disciplines are engaged in developing and manufacturing high-quality, highly functional, and nutritious food. However, these efforts are being increasingly challenged by a significant need for new tools, data, and information that are essential for winning the war against food fraud. The phenomenon of food fraud or adulteration is not new and has impacted human civilizations since the beginning of food production and trading. In most cases, food fraud is economically driven, however, its direct and indirect actual and potential influence reaches far beyond its economic impact on individuals and societies. In many cases, adulterated food contains harmful or toxic chemical, physical, and/or biological constituents that present significant food-safety-related risks. Food fraud leads to loss of reputation that spans throughout the entire food supply chain and damages consumer confidence. In many cases, intentionally adulterated or fraudulent foods present challenges that are related to food defense and/or food security. Food fraud can have social- dietary- and/or religion-related implications. Food fraud may also present potential homeland security-related implications (bioterrorism). It is thus essential to recognize that food fraud exhibits a very wide sphere of challenges that must be addressed collectively as food crimes. The annual global economic loss that results from food crimes is estimated to be about $70 billion and affects individuals, companies, regions, and national economies. Strategies and means that are aimed at enhancing our capability to identify and fight these crimes have therefore to be developed and implemented in a way that successfully addresses the multifaceted nature of food crimes. There are different ways by which food products, food raw materials, and ingredients, as well as information about them are adulterated. In its most basic form, food crime is based on counterfeiting and marketing illegal replication of a legal product. In other cases, food products are diluted with water or inferior/toxic ingredients. Food crimes may involve substitution of a high-value component of a product with inferior, lower-value, and/or unsafe ingredients. Adulteration of food may also involve concealment of defects or poor quality of a product or its components. In yet other cases, food crimes may involve enhancement of product or its ingredients with banned and/or toxic ingredients or application of an unapproved process to improve the product quality. Gray market theft and diversion as well as marketing of mislabeled products with incorrect or missing information about their nature or their regional origin are also common aspects of food crimes. In recent years, the regional origin of specific food products has gained a significant importance and value among consumers and thus has become a major target of adulteration. Identifying and fighting food crimes is highly dependent on our capability to effectively authenticate food products, their constituent raw materials, and food ingredients. In simple terms, authentication must provide answers to two questions: a) is this what it says it is? And b), in most cases, where does it come from? Answering these questions requires the application of a broad array of effective and credible analytical tools and approaches that can identify the fraudulent nature of a product in questions. Success in fighting food crimes is also determined by our ability to trace every product and/or its ingredient throughout the entire supply chain. Many analytical tools for food authentication exist, however, success also requires having a continuously validated and updated data bank consisting of the "fingerprints" of all such products and ingredients. These data sets should be developed in a way that reflects (for each product and ingredient) the inherent biological-, physical-, chemical-, regional- seasonal-, and agrotechnological-related variabilities. The latter mounts to a major knowledge gap that hinders our ability to effectively identify and fight food crimes. During recent decades, the food supply chain has been globalized and evolved from a relatively simple linear chain of regional transactions into a very complicated and convoluted series of events that span all over the globe. This evolution and the among-regions differences in analytical and traceability capabilities compromise success of food traceability and authentication and thus the deterrence of food fraud. The latter presents a major challenge that must be addressed. If food crime remains a "profitable low-risk criminal activity" it will continue to grow and flourish. This aspect is reflected in the significant involvement of organized crime in food crimes. A very effective legislation and enforcement effort is therefore needed at the local, regional, and international level to successfully fight food crimes. I would like to call upon funding agencies, academia, research institutes, and governments to direct means and efforts at developing and introducing the basic knowledge and applicable information that are needed in order to close the above-described knowledge and data gaps and affectively address the current challenges that are presented by food crimes. I would like to invite all those who will become engaged in such efforts to publish their work in our journal.
Moshe Rosenberg, D.Sc., Editor in Chief
Professor Emeritus Food Science and Technology
Department of Food Science and Technology
University of California, Davis
Davis, CA 95616
U.S.A
[1] |
Porfiri M and Di Bernardo M (2008) Criteria for global pinning-controllability of complex networks. Automatica 44: 3100-3106. doi: 10.1016/j.automatica.2008.05.006
![]() |
[2] |
Van Den Broek B, Wiegerinck W, Kappen B (2008) Graphical model inference in optimal control of stochastic multi-agent systems. J Artif Intell Res 32: 95-122. doi: 10.1613/jair.2473
![]() |
[3] |
Fornasier M and Solombrino F (2014) Mean-field optimal control. ESAIM: Control, Optimisation and Calculus of Variations 20: 1123-1152. doi: 10.1051/cocv/2014009
![]() |
[4] |
Wang Z, Lu R, Shen B (2014) Distributed estimation and control for general systems. Int J Gen Syst 43: 247-253. doi: 10.1080/03081079.2014.883710
![]() |
[5] |
Li S, Yao X, Li W (2020) Almost sure exponential stabilization of hybrid stochastic coupled systems via intermittent noises: A higher-order nonlinear growth condition. J Math Anal Appl 489: 124150. doi: 10.1016/j.jmaa.2020.124150
![]() |
[6] | Xu Y, Shen R, Li W (2019) Finite-time synchronization for coupled systems with time delay and stochastic disturbance under feedback control. J Appl Anal Comput 10: 1-24. |
[7] |
Herzallah R and Kárnỳ M (2017) Towards probabilistic synchronisation of local controllers. Int J Syst Sci 48: 604-615. doi: 10.1080/00207721.2016.1197979
![]() |
[8] |
Herzallah R (2011) Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty. International Journal of Modelling, Identification and Control 12: 311-327. doi: 10.1504/IJMIC.2011.040076
![]() |
[9] |
Herzallah R and Lowe D (2003) Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals. Neural Comput Appl 12: 98-108. doi: 10.1007/s00521-003-0375-y
![]() |
[10] |
Herzallah R and Lowe D (2007) Distribution modeling of nonlinear inverse controllers under a bayesian framework. IEEE T neural networks 18: 107-114. doi: 10.1109/TNN.2006.883721
![]() |
[11] |
Zhang QC, Hu L, Gow J (2020) Output feedback stabilization for mimo semi-linear stochastic systems with transient optimisation. International Journal of Automation and Computing 17: 83- 95. doi: 10.1007/s11633-019-1193-8
![]() |
[12] |
Zhang Q and Wang A (2016) Decoupling control in statistical sense: minimised mutual information algorithm. International Journal of Advanced Mechatronic Systems 7: 61-70. doi: 10.1504/IJAMECHS.2016.082625
![]() |
[13] | Ren M, Zhang Q, Zhang J (2019) An introductory survey of probability density function control. Systems Science & Control Engineering 7: 158-170. |
[14] |
Aji SM and McEliece RJ (2000) The generalized distributive law. IEEE T Inform Theory 46: 325- 343. doi: 10.1109/18.825794
![]() |
[15] | Pearl J (2014) Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier. |
[16] |
Gallager R (1962) Low-density parity-check codes. IRE Transactions on information theory 8: 21-28. doi: 10.1109/TIT.1962.1057683
![]() |
[17] | Mézard M, Parisi G, Virasoro M (1987) Spin glass theory and beyond: An Introduction to the Replica Method and Its Applications. World Scientific Publishing Company. |
[18] |
Zhou Y, Wang A, Zhou P, et al. (2020) Dynamic performance enhancement for nonlinear stochastic systems using rbf driven nonlinear compensation with extended kalman filter. Automatica 112: 108693. doi: 10.1016/j.automatica.2019.108693
![]() |
[19] | Zhou Y, Zhang Q, Wang H, et al. (2017) Ekf-based enhanced performance controller design for nonlinear stochastic systems. IEEE T Automat Contr 63: 1155-1162. |
[20] | Zhang Q, Zhou J, Wang H, et al. (2015) Minimized coupling in probability sense for a class of multivariate dynamic stochastic control systems. 2015 54th IEEE Conference on Decision and Control (CDC), 1846-1851. |
[21] | Herzallah R and Lowe D (2002) Improved robust control of nonlinear stochastic systems using uncertain models. |
[22] | Herzallah R and Lowe D (2006) Bayesian adaptive control of nonlinear systems with functional uncertainty. Proceedings of the 7th Portuguese Conference on Automatic Control. |
[23] |
Herzallah R (2018) Generalised probabilistic control design for uncertain stochastic control systems. Asian J Control 20: 2065-2074. doi: 10.1002/asjc.1717
![]() |
[24] | Zhou Y, Herzallah R, Zafar A (2019) Fully probabilistic design for stochastic discrete system with multiplicative noise. 2019 IEEE 15th International Conference on Control and Automation (ICCA), 940-945. |
[25] |
Kárnỳ M (1996) Towards fully probabilistic control design. Automatica 32: 1719-1722. doi: 10.1016/S0005-1098(96)80009-4
![]() |
[26] |
Kárnỳ M and Guy TV (2006) Fully probabilistic control design. Syst Control Lett 55: 259-265. doi: 10.1016/j.sysconle.2005.08.001
![]() |
[27] |
Herzallah R and Kárnỳ M (2011) Fully probabilistic control design in an adaptive critic framework. Neural networks 24: 1128-1135. doi: 10.1016/j.neunet.2011.06.006
![]() |
[28] |
Zhou Y and Herzallah R (2020) Dobc based fully probability design for stochastic system with the multiplicative noise. IEEE Access 8: 34225-34235. doi: 10.1109/ACCESS.2020.2974279
![]() |
[29] |
Kullback S and Leibler RA (1951) On information and sufficiency. The annals of mathematical statistics 22: 79-86. doi: 10.1214/aoms/1177729694
![]() |
[30] |
Peterka V (1981) Bayesian system identification. Automatica 17: 41-53. doi: 10.1016/0005-1098(81)90083-2
![]() |
[31] |
Herzallah R (2013) Probabilistic dhp adaptive critic for nonlinear stochastic control systems. Neural Networks 42: 74-82. doi: 10.1016/j.neunet.2013.01.014
![]() |
[32] | Alessio A and Bemporad A (2007) Decentralized model predictive control of constrained linear systems. 2007 European Control Conference (ECC), 2813-2818. |