Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

The focus of this work is on Statistical Process Control (SPC) of a manufacturing process based on available measurements. Two important applications of SPC in industrial settings are fault detection and diagnosis (FDD). In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples. The explainability is quantified by a novel relevance measure of input variables that is calculated from a Layerwise Relevance Propagation (LRP) algorithm. It is shown that the relevances can be used to discard redundant input feature vectors/ variables iteratively thus resulting in reduced over-fitting of noisy data, increasing distinguishability between output classes and superior FDD test accuracy. The efficacy of the proposed method is demonstrated on the benchmark Tennessee Eastman Process.Comment: Under Review. arXiv admin note: text overlap with arXiv:2012.0386

Comparison of Alternative Sowing Methods in Hungarian Vetch and Triticale Cultivation in Terms of Yield and Weed Biomass

This study aimed to compare alternative sowing methods i.e., sole, mix and intercrops sown in alternative and perpendicular rows on the productivity of Hungarian vetch-triticale farming. Similarly, the effects of these sowing methods were also inferred on weed infestation. This study was conducted during the vegetation periods of 2018-19 and 2019-20 in terrestrial climate conditions. Sowing in perpendicular rows proved more effective in suppressing weed infestation than alternative rows and increased yield by reducing weed biomass. Sowing Hungarian vetch in mixed and perpendicular rows observed the highest weed infestation and biomass along with low green herbage and dry matter yield. However, Hungarian vetch-triticale mixture (50-50%) sowed in perpendicular rows made the best use of available land, resulted in the lowest weed infestation and biomass and recorded the highest green herbage and dry matter yield. In conclusion, the sowing Hungarian vetch-triticale mixture (50-50%) in perpendicular rows was the most suitable sowing method for improving productivity and suppressing weed infestation in Hungarian vetch-triticale intercropping

Online and Offline Dynamic Influence Maximization Games Over Social Networks

In this work, we consider dynamic influence maximization games over social networks with multiple players (influencers). The goal of each influencer is to maximize their own reward subject to their limited total budget rate constraints. Thus, influencers need to carefully design their investment policies considering individuals' opinion dynamics and other influencers' investment strategies, leading to a dynamic game problem. We first consider the case of a single influencer who wants to maximize its utility subject to a total budget rate constraint. We study both offline and online versions of the problem where the opinion dynamics are either known or not known a priori. In the singe-influencer case, we propose an online no-regret algorithm, meaning that as the number of campaign opportunities grows, the average utilities obtained by the offline and online solutions converge. Then, we consider the game formulation with multiple influencers in offline and online settings. For the offline setting, we show that the dynamic game admits a unique Nash equilibrium policy and provide a method to compute it. For the online setting and with two influencers, we show that if each influencer applies the same no-regret online algorithm proposed for the single-influencer maximization problem, they will converge to the set of $\epsilon$-Nash equilibrium policies where $\epsilon=O(\frac{1}{\sqrt{K}})$ scales in average inversely with the number of campaign times $K$ considering the average utilities of the influencers. Moreover, we extend this result to any finite number of influencers under more strict requirements on the information structure. Finally, we provide numerical analysis to validate our results under various settings.Comment: This work has been submitted to IEEE for possible publicatio

Weighted Age of Information based Scheduling for Large Population Games on Networks

In this paper, we consider a discrete-time multi-agent system involving $N$ cost-coupled networked rational agents solving a consensus problem and a central Base Station (BS), scheduling agent communications over a network. Due to a hard bandwidth constraint on the number of transmissions through the network, at most $R_d < N$ agents can concurrently access their state information through the network. Under standard assumptions on the information structure of the agents and the BS, we first show that the control actions of the agents are free of any dual effect, allowing for separation between estimation and control problems at each agent. Next, we propose a weighted age of information (WAoI) metric for the scheduling problem of the BS, where the weights depend on the estimation error of the agents. The BS aims to find the optimum scheduling policy that minimizes the WAoI, subject to the hard bandwidth constraint. Since this problem is NP hard, we first relax the hard constraint to a soft update rate constraint, and then compute an optimal policy for the relaxed problem by reformulating it into a Markov Decision Process (MDP). This then inspires a sub-optimal policy for the bandwidth constrained problem, which is shown to approach the optimal policy as $N \rightarrow \infty$. Next, we solve the consensus problem using the mean-field game framework wherein we first design decentralized control policies for a limiting case of the $N$-agent system (as $N \rightarrow \infty$). By explicitly constructing the mean-field system, we prove the existence and uniqueness of the mean-field equilibrium. Consequently, we show that the obtained equilibrium policies constitute an $\epsilon$-Nash equilibrium for the finite agent system. Finally, we validate the performance of both the scheduling and the control policies through numerical simulations.Comment: This work has been submitted to IEEE for possible publicatio

Epidemiology of pemphigus in Turkey: One-year prospective study of 220 cases

Pemphigus is a group of rare and life-threatening autoimmune blistering diseases of the skin and mucous membranes. Although they occur worldwide, their incidence shows wide geographical variation, and prospective data on the epidemiology of pemphigus are very limited. Objective of this work is to evaluate the incidence and epidemiological and clinical features of patients with pemphigus in Turkey. All patients newly diagnosed with pemphigus between June 2013 and June 2014 were prospectively enrolled in 33 dermatology departments in 20 different provinces from all seven regions of Turkey. Disease parameters including demography and clinical findings were recorded. A total of 220 patients were diagnosed with pemphigus during the 1-year period, with an annual incidence of 4.7 per million people in Turkey. Patients were predominantly women, with a male to female ratio of 1:1.41. The mean age at onset was 48.9 years. Pemphigus vulgaris (PV) was the commonest clinical subtype (n=192; 87.3%), followed by pemphigus foliaceus (n=21; 9.6%). The most common clinical subtype of PV was the mucocutaneous type (n=83; 43.2%). The mean Pemphigus Disease Area Index was 28.14±22.21 (mean ± Standard Deviation).  The incidence rate of pemphigus in Turkey is similar to the countries of South-East Europe, higher than those reported for the Central and Northern European countries and lower than the countries around the Mediterranean Sea and Iran. Pemphigus is more frequent in middle-aged people and is more common in women. The most frequent subtype was PV, with a 9-fold higher incidence than pemphigus foliaceus.   </p