In this paper, a novel and effective stiction detection method is proposed by combining K-means clustering and the moving window approach. As a byproduct, the proposed stiction detection method offers an estimation for the stiction band in sticky control valves. The proposed stiction detection method is tested in industrial case studies consisting of benchmark industrial control loops and control loops from an oil sands industry. In the benchmark industrial control loops, the results of the proposed method are compared with some of the existing stiction detection methods. This comparison shows superior performance of the proposed method. It is noticed through a simulation case study and an industrial case study that the proposed method not only provides stiction band estimation but also can detect severe valve stiction or unexpected valve closures.
The BacT/ALERT® 3D system was validated to determine the sterility of different types of biopharmaceutical samples such as water for injection, unprocessed bulk, and finished bulk. The installation, operation, and performance qualification were completed and verified under good manufacturing practices. During the installation and operation validation stages, the functionality and security of the system and software were completed and verified. For the performance qualification, 11 microorganisms were evaluated, six compendial (Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus subtilis, Candida albicans, Aspergillus niger, Clostridium sporogenes), one representing the number one microbial species in sterile product recalls (Burkholderia cepacia), and four environmental isolates (Kocuria rhizophila, Staphylococcus haemolyticus, Methylobacterium radiotolerans, and Penicillium spp.). Nine of the microorganisms were spiked into three different types of biopharmaceutical samples by three different analysts on different days to ascertain the equivalence, ruggedness, sensitivity, time of detection, and repeatability. In all samples, the BacT/ALERT® exhibited equivalent or better detection than the standard test. With the exception of M. radiotolerans, all 11 microorganisms were detected within 2.5 days using the BacT/ALERT® system and the standard test. The detection times for M. radiotolerans in the three sample types averaged 5.77 days. The minimum detectable level of cells for all the microorganisms tested was found to be within 1 to 2 CFU. The system optimized sterility testing by the simultaneous on-line, non-destructive incubation and detection of microbial growth.
Wireless networking is popular due to the “3 any” concept: anyone, anytime, anywhere. Wireless communication technology advancements have covered the opportunities for sustainable development of low-power, low-cost, multipurpose sensor nodes in wireless sensor networks. In sensor networks, the network layer handles routing problems. Since radio transmission requires a significant amount of energy, it is essential to investigate power efficiency and optimization. As a result, the conservation of energy is a critical concern in wireless sensor networks. Recent research is focused on developing routing algorithms that use less amount of energy during communication, thereby prolonging the network’s life. Wireless sensor networks with energy recovery nodes use nodes that can extract energy from their environment. The fuzzy-GWO method and the energy-saving routing algorithm are proposed and analyzed in this research work. For simulation, the MATLAB 2021b working environment is used. The LEACH, HEED, MBC, FRLDG protocols, along with the proposed protocol F-GWO, are compared. From the obtained results, it is found that the network lifetime is increased by 20%, 14.8%, 12.5%, and 3.8%, respectively. In addition, the proposed method has a 37.5%, 33.3%, 16.6%, and 6.25% reduction in average energy consumption when compared with the conventional algorithms. According to the experimental data obtained through simulation, the proposed F-GWO algorithm outperforms the LEACH, HEED, MBC, and FRLDG in network lifetime, packet delivery ratio, throughput, bit error rate (BER), buffer occupancy, time analysis, and end-to-end delay.
Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful to detect and classify the different grades of ICH. Because of the latest advancement of deep learning (DL) models on image processing applications, several medical imaging techniques utilize it. This study develops a new densely connected convolutional network (DenseNet) with extreme learning machine (ELM)) for ICH diagnosis and classification, called DN-ELM. The presented DL-ELM model utilizes Tsallis entropy with a grasshopper optimization algorithm (GOA), named TEGOA, for image segmentation and DenseNet for feature extraction. Finally, an extreme learning machine (ELM) is exploited for image classification purposes. To examine the effective classification outcome of the proposed method, a wide range of experiments were performed, and the results are determined using several performance measures. The simulation results ensured that the DL-ELM model has reached a proficient diagnostic performance with the maximum accuracy of 96.34%.
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