Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes

Batch process monitoring is a challenging task, because conventional methods are not well suited to handle the inherent multiphase operation. In this study, a novel multiway independent component analysis (MICA) mixture model and mutual information based fault detection and diagnosis approach is proposed. The multiple operating phases in batch processes are characterized by non‐Gaussian independent component mixture models. Then, the posterior probability of the monitored sample is maximized to identify the operating phase that the sample belongs to, and, thus, the localized MICA model is developed for process fault detection. Moreover, the detected faulty samples are projected onto the residual subspace, and the mutual information based non‐Gaussian contribution index is established to evaluate the statistical dependency between the projection and the measurement along each process variable. Such contribution index is used to diagnose the major faulty variables responsible for process abnormalities. The effectiveness of the proposed approach is demonstrated using the fed‐batch penicillin fermentation process, and the results are compared to those of the multiway principal component analysis mixture model and regular MICA method. The case study demonstrates that the proposed approach is able to detect the abnormal events over different phases as well as diagnose the faulty variables with high accuracy. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2761–2779, 2013     

Synthesis and characterization of polycaprolactone/acrylic acid (PCL/AA) hydrogel for controlled drug delivery

In the present work biodegradable pH-sensitive polycaprolactone/acrylic acid (PCL/AA) hydrogels have been developed using ethylene glycol dimethacrylate (EGDMA) as a cross-linker and benzoyl peroxide as initiator. For these prepared hydrogels swelling studies, sol-gel fraction analysis and porosity measurements were performed. Results show that swelling of the hydrogels decreases on increasing the concentration of PCL and EGDMA, however swelling of hydrogels increases on increasing the concentration of AA. Results of sol-gel fraction analysis show that gel fraction increases on increasing concentration of monomer AA, polymer PCL as well as cross-linker EGDMA. As far as porosity is concerned, it increases on increasing the concentration of AA and PCL while porosity decreases on increasing the concentration of EGDMA. Hydrogels were characterized by measuring diffusion coefficient (D) and equilibrium water content (EWC). Network formation, morphology and crystallinity of PCL/AA hydrogels were investigated using FTIR, SEM and XRD, respectively. Tramadol hydrochloride was loaded as model drug and its release pattern was analysed using various kinetic models like zero order, first order, Higuchi and Peppas. Results indicated that most of the samples followed non-Fickian release mechanism.     

Salient object detection employing regional principal color and texture cues

Multimedia Tools and Applications - Saliency in a scene describes those facets of any stimulus that makes it stand out from the masses. Saliency detection has attracted numerous algorithms in...     

Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

Secret key generation (SKG) is an emerging technology to secure wireless communication from attackers. Therefore, the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’ uncertainty. However, the physical layer secret key generation (PHY-SKG) depends on two fundamental parameters, i.e., coherence time and power allocation. The coherence time for PHY-SKG is not applicable to secure wireless channels. This is because coherence time is for a certain period of time. Thus, legitimate users generate the secret keys (SKs) with a shorter key length in size. Hence, an attacker can quickly get information about the SKs. Consequently, the attacker can easily get valuable information from authentic users. Therefore, we considered the scheme of power allocation to enhance the secret key generation rate (SKGR) between legitimate users. Hence, we propose an alternative method, i.e., a power allocation, to improve the SKGR. Our results show 72% higher SKGR in bits/sec by increasing power transmission. In addition, the power transmission is based on two important parameters, i.e., epsilon and power loss factor, as given in power transmission equations. We found out that a higher value of epsilon impacts power transmission and subsequently impacts the SKGR. The SKGR is approximately 40.7% greater at 250 from 50 mW at epsilon = 1. The value of SKGR is reduced to 18.5% at 250 mW when epsilonis 0.5. Furthermore, the transmission power is also measured against the different power loss factor values, i.e., 3.5, 3, and 2.5, respectively, at epsilon = 0.5. Hence, it is concluded that the value of epsilon and power loss factor impacts power transmission and, consequently, impacts the SKGR.     

Investigation into ultraviolet radiations from modern electric light sources

Due to concerns of increasing electrical energy demand and government regulations, traditional incandescent lamps are being replaced by energy-efficient lamps, such as compact fluorescent lamps (CFLs) and light emitting diodes (LEDs). The widespread adoption of new solid-state light sources requires investigation into several effects associated with these alternative light sources. One of the important concerns is ultraviolet (UV) radiations coming out of these new light sources. This brief report presents the experimental data obtained on UV radiations from CFLs and LEDs.     

A novel dynamic bayesian network‐based networked process monitoring approach for fault detection,propagation identification,and root cause diagnosis

A novel networked process monitoring, fault propagation identification, and root cause diagnosis approach is developed in this study. First, process network structure is determined from prior process knowledge and analysis. The network model parameters including the conditional probability density functions of different nodes are then estimated from process operating data to characterize the causal relationships among the monitored variables. Subsequently, the Bayesian inference‐based abnormality likelihood index is proposed to detect abnormal events in chemical processes. After the process fault is detected, the novel dynamic Bayesian probability and contribution indices are further developed from the transitional probabilities of monitored variables to identify the major faulty effect variables with significant upsets. With the dynamic Bayesian contribution index, the statistical inference rules are, thus, designed to search for the fault propagation pathways from the downstream backwards to the upstream process. In this way, the ending nodes in the identified propagation pathways can be captured as the root cause variables of process faults. Meanwhile, the identified fault propagation sequence provides an in‐depth understanding as to the interactive effects of faults throughout the processes. The proposed approach is demonstrated using the illustrative continuous stirred tank reactor system and the Tennessee Eastman chemical process with the fault propagation identification results compared against those of the transfer entropy‐based monitoring method. The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2348–2365, 2013     

Study of single-cell massive MIMO systems with channel aging and prediction

Wireless Networks - In this paper we consider a single-cell massive multiple-input-multiple-output scenario, where the number of base station (BS) antennas is larger than the number of single...     

Handling sensor faults in economic model predictive control of batch processes

The problem of sensor fault detection and isolation (FDI) and fault-tolerant economic model predictive control (FT-EMPC) for batch processes is addressed. To this end, we first model batch processes using subspace-based system identification techniques. The analytical redundancy within the identified model is subsequently exploited to detect, isolate, and handle the faulty measurements. The reconciled fault-free measurements are then incorporated in an economic model predictive controller formulation. Simulation case studies involving the application of the proposed data-driven FDI and FT-EMPC algorithms to the energy intensive electric arc furnace process illustrate the improvement in economic performance under various fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 617–628, 2019