Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller

Molecular dynamics simulations of oil recovery from dolomite slit nanopores enhanced by CO2 and N2 injection

Shale oil reservoirs are dominated by micro-and nanopores, which greatly impede the oil recovery rates. CO2 and N2 injection have proven to be highly effective approaches to enhance oil recovery from low-permeability shale reservoirs, and also represent great potential for CO2 sequestration. Therefore, a better understanding of the mechanism of shale oil recovery enhanced by CO2 and N2 is of great importance to achieve maximum shale oil productivity. In this paper, the adsorption behavior of shale oil and the mechanism of enhancing shale oil recovery by CO2 and N2 flooding in dolomite slit pores are investigated by performing nonequilibrium molecular dynamics simulations. Considering the shale oil adsorption behavior, mass density distribution is analyzed and the results indicate that a symmetric density distribution of the oil regarding the center in the slit pore along the x-axis can be obtained. The maximum density of the adsorbed layer nearest to the slit wall is 1.310 g/cm3 for C8H18 , which is about 2.0 times of that for bulk oil density in the middle area of slit pore. The interaction energy and radial distribution functions (between oil and CO2 , and between oil and N2 ) are calculated to display the displacement behavior of CO2 and N2 flooding. It is found that CO2 and N2 play different roles: CO2 has strong solubility, diffusivity and a higher interaction energy with dolomite wall, and the oil displacement efficiency of CO2 reaches 100% after 1 ns of flooding; however, during N2 flooding, the oil displacement efficiency is 87.3% after 4 ns of flooding due to the lower interaction energy between N2 and dolomite and that between N2 and oil.Cited as: Guo, H., Wang, Z., Wang, B., Zhang, Y., Meng, H., Sui H. Molecular dynamics simulations of oil recovery from dolomite slit nanopores enhanced by CO2 and N2 injection. Advances in Geo-Energy Research, 2022, 6(4): 306-313. https://doi.org/10.46690/ager.2022.04.0

EdgeSense: Edge-Mediated Spatial-Temporal Crowdsensing

Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called Edge-Mediated Spatial-Temporal Crowdsensing. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, EdgeSense works on top of a secured peer-To-peer network consisted of participants and propose a novel Decentralized Spatial-Temporal Crowdsensing framework based on Parallelized Stochastic Gradient Descent. To approximate the sensing data in each part of the target area in each sensing cycle, EdgeSense uses the local sensor data in participants\u27 mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the EdgeSense on the real-world data sets (temperature [1] and PM2.5 [2] data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism

Early Detection of Disease using Electronic Health Records and Fisher\u27s Wishart Discriminant Analysis

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and linear inseparability of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher\u27s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of potential inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and weighted-averages the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification

A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services

The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retraining a model from scratch to fulfill the unlearning request is impractical due to the lack of training data on the service provider's side (the server). Furthermore, approximate unlearning further embraces a complex trade-off between utility (model performance) and privacy (unlearning performance). In this paper, we try to explore the potential threats posed by unlearning services in MLaaS, specifically over-unlearning, where more information is unlearned than expected. We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing, under black-box access settings, in which the existing machine unlearning attacks are not applicable. The effectiveness of these strategies is evaluated through extensive experiments on benchmark datasets, across various model architectures and representative unlearning approaches. Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios. This study uncovers an underexplored gap between unlearning and contemporary MLaaS, highlighting the need for careful considerations in balancing data unlearning, model utility, and security.Comment: To Appear in the Network and Distributed System Security Symposium (NDSS) 2024, San Diego, CA, US

A unique subseafloor microbiosphere in the Mariana Trench driven by episodic sedimentation

Hadal trenches are characterized by enhanced and infrequent high-rate episodic sedimentation events that likely introduce not only labile organic carbon and key nutrients but also new microbes that significantly alter the subseafloor microbiosphere. Currently, the role of high-rate episodic sedimentation in controlling the composition of the hadal subseafloor microbiosphere is unknown. Here, analyses of carbon isotope composition in a ~ 750 cm long sediment core from the Challenger Deep revealed noncontinuous deposition, with anomalous 14C ages likely caused by seismically driven mass transport and the funneling effect of trench geomorphology. Microbial community composition and diverse enzyme activities in the upper ~ 27 cm differed from those at lower depths, probably due to sudden sediment deposition and differences in redox condition and organic matter availability. At lower depths, microbial population numbers, and composition remained relatively constant, except at some discrete depths with altered enzyme activity and microbial phyla abundance, possibly due to additional sudden sedimentation events of different magnitude. Evidence is provided of a unique role for high-rate episodic sedimentation events in controlling the subsurface microbiosphere in Earth’s deepest ocean floor and highlight the need to perform thorough analysis over a large depth range to characterize hadal benthic populations. Such depositional processes are likely crucial in shaping deep-water geochemical environments and thereby the deep subseafloor biosphere