Optimalni neizraziti reglutor tipa 2 za sustave za grijanje, ventilaciju i klimatizaciju

In this paper a novel Optimal Type-2 Fuzzy Proportional-Integral-Derivative Controller (OT2FPIDC) is designed for controlling the air supply pressure of Heating, Ventilation and Air-Conditioning (HVAC) system. The parameters of input and output membership functions, and PID controller coefficients are optimized simultaneously by random inertia weight Particle Swarm Optimization (RNW-PSO). Simulation results show the superiority of the proposed controller than similar non-optimal fuzzy controller.U radu je predložena nova upravljačka shema optimalnog neizrazitog PID regulatora tipa 2 za upravljanje sustavima za grijajne, ventilaciju i klimatizaciju. Predložena je shema zasnovana na neizrazitom regulatoru (FLC) učestalo korištenom za upravljajne nelinearnim procesima. Kako bi se premostio problem neizrazitih regulatora, neodstatak metode dizajnirajna, parametri ulazno-izlaznih funkcija pripadanja, kao i parametri PID regulatora se optimiraju metodom roja čestica sa slučajnim parametrima inercije (RNW-PSO). Simulacijski rezultati pokazuju izvedivost predloženog pristupa

Cardiac MR segmentation based on sequence propagation by deep learning.

Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it extremely difficult to accurately and quickly manually segment the myocardial. In this paper, we propose a method for CMR segmentation based on U-Net and combined with image sequence information. The method can effectively segment from the top slice to the bottom slice of the CMR. During training, each input slice depends on the slice below it. In other words, the predicted segmentation result depends on the existing segmentation label of the previous slice. 3D sequence information is fully utilized. Our method was validated on the ACDC dataset, which included CMR images of 100 patients (1700 2D MRI). Experimental results show that our method can segment the myocardial quickly and efficiently and is better than the current state-of-the-art methods. When evaluating 340 CMR image, our model yielded an average dice score of 85.02 ± 0.15, which is much higher than the existing classical segmentation method(Unet, Dice score = 0.78 ± 0.3)

Target integrity assessment based on image and track information

The rapid and accurate integrity assessment of targets can provide important guarantee and reference for the subsequent decision of the implementer. The current research on target integrity assessment mainly adopts single data source or complex probability model, which leads to inability to balance the needs of accuracy and real-time. In order to solve this problem, a new target integrity assessment method based on image and track information is proposed in this paper. Image texture, corner points and track parameters before and after the execution of the target are used to transform the integrity assessment issue into a classification issue, and a comprehensive assessment result is made by combining various classification results. The experimental results show that the assessment results based on both image and track information reached 97.5%, higher than the evaluation results from a single data source, and the evaluation time was controlled in milliseconds, which not only improved the accuracy rate but also ensured the real-time assessment

ICANet: a simple cascade linear convolution network for face recognition

Abstract Recently, deep convolutional networks have demonstrated their capability of improving the discriminative power compared with other machine learning method, but its feature learning mechanism is not very clear. In this paper, we present a cascaded linear convolutional network, based on independent component analysis (ICA) filters, named ICANet. ICANet consists of three parts: a convolutional layer, a binary hash, and a block histogram. It has the following advantages over other methods: (1) the network structure is simple and computationally efficient, (2) the ICA filter is trained with an unsupervised algorithm using unlabeled samples, which is practical, and (3) compared to deep learning models, each layer parameter in ICANet can be easily trained. Thus, ICANet can be used as a benchmark for the application of a deep learning framework for large-scale image classification. Finally, we test two public databases, AR and FERET, showing that ICANet performs well in facial recognition tasks

Research on cogging torque optimization design of permanent magnet synchronous wind turbine

Cogging torque is one of the unique problems of permanent magnet generators. Its main cause is the uneven distribution of the generator’s magnetic permeability, which directly affects the starting and running performance of the generator. The study of cogging torque suppression methods is of great significance for improving the operating stability and service life of generators. Through the analysis of the principle of cogging torque, an optimization method for the amplitude of cogging torque based on Taguchi algorithm for the two parameters of pole arc coefficient and skew angle is established. And the finite element analysis method is used to quantitatively compare the characteristic parameters of the generator model before and after the optimization by Taguchi algorithm. The results show that when the pole arc coefficient and the angle of the chute are in the optimal value at the same time, the cogging torque of the generator can be greatly reduced, and its air gap magnetic density waveform and induced electromotive force waveform are ideal, which provides a research method for the design and parameter optimization of large megawatt permanent magnet synchronous wind turbines

Hardware Trojan Detection Based on Ordered Mixed Feature GEP

In the hardware Trojan detection field, destructive reverse engineering and bypass detection are both important methods. This paper proposed an evolutionary algorithm called Ordered Mixed Feature GEP (OMF-GEP), trying to restore the circuit structure only by using the bypass information. This algorithm was developed from the basic GEP through three sets of experiments at different stages. To solve the problem, this paper transformed the GEP by introducing mixed features, ordered genes, and superchromosomes. And the experiment results show that the algorithm is effective

Revealing the Pharmacological Mechanism of Acorus tatarinowii in the Treatment of Ischemic Stroke Based on Network Pharmacology

Aim. Stroke is the second significant cause for death, with ischemic stroke (IS) being the main type threatening human being’s health. Acorus tatarinowii (AT) is widely used in the treatment of Alzheimer disease, epilepsy, depression, and stroke, which leads to disorders of consciousness disease. However, the systemic mechanism of AT treating IS is unexplicit. This article is supposed to explain why AT has an effect on the treatment of IS in a comprehensive and systematic way by network pharmacology. Methods and Materials. ADME (absorbed, distributed, metabolized, and excreted) is an important property for screening-related compounds in AT, which were screening out of TCMSP, TCMID, Chemistry Database, and literature from CNKI. Then, these targets related to screened compounds were predicted via Swiss Targets, when AT-related targets database was established. The gene targets related to IS were collected from DisGeNET and GeneCards. IS-AT is a common protein interactive network established by STRING Database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analysed by IS-AT common target genes. Cytoscape software was used to establish a visualized network for active compounds-core targets and core target proteins-proteins interactive network. Furthermore, we drew a signal pathway picture about its effect to reveal the basic mechanism of AT against IS systematically. Results. There were 53 active compounds screened from AT, inferring the main therapeutic substances as follows: bisasaricin, 3-cyclohexene-1-methanol-α,α,4-trimethyl,acetate, cis,cis,cis-7,10,13-hexadecatrienal, hydroxyacoronene, nerolidol, galgravin, veraguensin, 2′-o-methyl isoliquiritigenin, gamma-asarone, and alpha-asarone. We obtained 398 related targets, 63 of which were the same as the IS-related genes from targets prediction. Except for GRM2, remaining 62 target genes have an interactive relation, respectively. The top 10 degree core target genes were IL6, TNF, IL1B, TLR4, NOS3, MAPK1, PTGS2, VEGFA, JUN, and MMP9. There were more than 20 terms of biological process, 7 terms of cellular components, and 14 terms of molecular function through GO enrichment analysis and 13 terms of signal pathway from KEGG enrichment analysis based on P<0.05. Conclusion. AT had a therapeutic effect for ischemic via multicomponent, multitarget, and multisignal pathway, which provided a novel research aspect for AT against IS

Effects of brain network segregation and integration on motor imagery sensorimotor rhythm

Diverse cognitive processes place different demands on locally segregated and globally integrated brain activities. Motor imagery (MI) is a complex mental operation characterized by sensorimotor rhythms. However, how the brain network acts on MI rhythms is not well-known. The present work aimed to explore the effects of brain integration and segregation on brain rhythmic oscillations. The power spectrum dynamics, topography distribution, and brain network metrics in the alpha and beta bands were calculated. And the correlations were investigated with the network metrics and sensorimotor rhythm. The results showed that the degree of event-related desynchronization/synchronization (ERD/ERS) was higher in alpha band than in beta band during [−1, 1 s] (p < 0.01). The topography of the alpha band demonstrated a bilateral distribution during MI processing, while the beta band had more diffuse distributions around the centre. Moreover, global efficiency was associated with bilateral ERD, and the transitivity was related to contralateral local power. These results suggested that network functions could facilitate the completion of behavior tasks. The integration was related to bilateral hemisphere coordination and the segregation was related to local activation, which shaped the local neural modulation of individuals in MI

Mechanisms of improved aortic stiffness by arotinolol in spontaneously hypertensive rats.

OBJECTIVES: This study investigates the effects on aortic stiffness and vasodilation by arotinolol and the underlying mechanisms in spontaneously hypertensive rats (SHR). METHODS: The vasodilations of rat aortas, renal and mesenteric arteries were evaluated by isometric force recording. Nitric oxide (NO) was measured in human aortic endothelial cells (HAECs) by fluorescent probes. Sixteen-week old SHRs were treated with metoprolol (200 mg·kg-1·d⁻¹), arotinolol (30 mg·kg-1·d⁻¹) for 8 weeks. Central arterial pressure (CAP) and pulse wave velocity (PWV) were evaluated via catheter pressure transducers. Collagen was assessed by immunohistochemistry and biochemistry assay, while endothelial nitric oxide synthase (eNOS) and eNOS phosphorylation (p-eNOS) of HAECs or aortas were analyzed by western blotting. RESULTS: Arotinolol relaxed vascular rings and the relaxations were attenuated by Nω-nitro-L-arginine methyl ester (L-NAME, NO synthase inhibitor) and the absence of endothelium. Furthermore, arotinolol-induced relaxations were attenuated by 4-aminopyridine (4-AP, Kv channels blocker). Arotinolol produced more nitric oxide compared to metoprolol and increased the expression of p-eNOS in HAECs. These results indicated that arotinolol-induced vasodilation involves endothelium-derived NO and Kv channels. The treatement with arotinolol in 8 weeks, but not metoprolol, markedly decreased CAP and PWV. Biochemistry assay and immunohistochemistry showed that aortic collagen depositions in the arotinolol groups were reduced compared with SHRs with metoprolol. Moreover, eNOS phosphorylation was significantly increased in aortinolol-treated SHR compared with SHRs with metoprolol. CONCLUSIONS: Arotinolol improves arterial stiffness in SHR, which involved in increasing NO and decreasing collagen contents in large arteries