Adaptive Fault Diagnosis of Motors Using Comprehensive Learning Particle Swarm Optimizer with Fuzzy Petri Net

This study proposes and applies a comprehensive learning particle swarm optimization (CLPSO) fuzzy Petri net (FPN) algorithm, which is based on the CLPSO algorithm and FPN, to the fault diagnosis of a complex motor. First, the transition confidence is replaced by a Gaussian function to deal with the uncertainty of fault propagation. Then, according to the Petri net principle, a competition operator is introduced to improve the matrix reasoning. Finally, a CLPSO-FPN model for motor fault diagnosis is established based on the motor failure mechanism and fault characteristics. The CLPSO algorithm is used to generate the system parameters for fault diagnosis and to improve the adaptability and accuracy of fault diagnosis. This study considers the example of a three-phase asynchronous motor. The results show that the proposed algorithm can diagnose faults in this motor with satisfactory adaptability and accuracy compared with the traditional FPN algorithm. By establishing the system model, the fault propagation process of motors can be accurately and intuitively expressed, thus improving the fault treatment and equipment maintenance of motors

FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learning from compiler intermediate representations (IRs). Existing IR-based models typically utilize IR features such as instructions, control and data flow graphs (CDFGs), call graphs, etc. However, these methods confuse variable nodes and instruction nodes in a CDFG and fail to distinguish different types of flows, and the neural networks they use fail to capture long-distance dependencies and have over-smoothing and over-squashing problems. To address these weaknesses, we propose FAIR, a Flow type-Aware pre-trained model for IR that involves employing (1) a novel input representation of IR programs; (2) Graph Transformer to address over-smoothing, over-squashing and long-dependencies problems; and (3) five pre-training tasks that we specifically propose to enable FAIR to learn the semantics of IR tokens, flow type information, and the overall representation of IR. Experimental results show that FAIR can achieve state-of-the-art results on four code-related downstream tasks.Comment: ICSE 2024 First Cycl

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe

Ultrasound-targeted microbubble destruction mediated herpes simplex virus-thymidine kinase gene treats hepatoma in mice

<p>Abstract</p> <p>Objective</p> <p>The purpose of the study was to explore the anti-tumor effect of ultrasound -targeted microbubble destruction mediated herpes simplex virus thymidine kinase (HSV-TK) suicide gene system on mice hepatoma.</p> <p>Methods</p> <p>Forty mice were randomly divided into four groups after the models of subcutaneous transplantation tumors were estabilished: (1) PBS; (2) HSV-TK (3) HSV-TK+ ultrasound (HSV-TK+US); (4) HSV-TK+ultrasound+microbubbles (HSV-TK+US+MB). The TK protein expression in liver cancer was detected by western-blot. Applying TUNEL staining detected tumor cell apoptosis. At last, the inhibition rates and survival time of the animals were compared among all groups.</p> <p>Results</p> <p>The TK protein expression of HSV-TK+MB+US group in tumor-bearing mice tissues were significantly higher than those in other groups. The tumor inhibitory effect of ultrasound-targeted microbubble destruction mediated HSV-TK on mice transplantable tumor was significantly higher than those in other groups (p < 0.05), and can significantly improve the survival time of tumor-bearing mice.</p> <p>Conclusion</p> <p>Ultrasound-targeted microbubble destruction can effectively transfect HSV-TK gene into target tissues and play a significant inhibition effect on tumors, which provides a new strategy for gene therapy in liver cancer.</p

Detection of the recombinant proteins in single transgenic microbial cells using laser tweezers and Raman spectroscopy,” Anal

Laser tweezers Raman spectroscopy (LTRS) has been used for the rapid detection of recombinant somatolactin protein produced in single Escherichia coli bacteria and Pichia pastoris yeast cell in the current study. A cDNA sequence encoding mature peptide of zebrafish somatolactin was inserted into two different expression vectors and transfected into E. coli or P. pastoris yeast cells. We measured Raman spectra of single E. coli cells at different culture times following the induction with isopropyl -D-1-thiogalactopyranoside, from which the amount of the generated somatolactin proteins was obtained by the projection of the entire cell&apos;s spectrum onto the spectrum of the pure somatolactin proteins or the dot product between these two spectral vectors. We found that the intensity of the somatolactin protein-associated spectra from single E. coli cells increased as the function of the culture time, which correlates with the accumulation of recombinant proteins inside the cells. This spectral observation was supported by evidence obtained by conventional methods of sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blotting analyses. The increased intensities of recombinant protein-associated Raman bands were also observed in another expression system, P. pastoris yeast cells. These findings demonstrate that the LTRS is a useful method for rapid sensing of recombination production in single host microorganism in vivo

A Novel Maximum Power Point Tracking Algorithm Based on Glowworm Swarm Optimization for Photovoltaic Systems

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O) and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP). In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions

Empagliflozin inhibits coronary microvascular dysfunction and reduces cardiac pericyte loss in db/db mice

BackgroundCoronary microvascular dysfunction (CMD) is a pathophysiological feature of diabetic heart disease. However, whether sodium-glucose cotransporter 2 (SGLT2) inhibitors protect the cardiovascular system by alleviating CMD is not known.ObjectiveWe observed the protective effects of empagliflozin (EMPA) on diabetic CMD.Materials and methodsThe mice were randomly divided into a db/db group and a db/db + EMPA group, and db/m mice served as controls. At 8 weeks of age, the db/db + EMPA group was given empagliflozin 10 mg/(kg⋅d) by gavage for 8 weeks. Body weight, fasting blood glucose and blood pressure were dynamically observed. Cardiac systolic and diastolic function and coronary flow reserve (CFR) were detected using echocardiography. The coronary microvascular structure and distribution of cardiac pericytes were observed using immunofluorescence staining. Picrosirius red staining was performed to evaluate cardiac fibrosis.ResultsEmpagliflozin lowered the increased fasting blood glucose levels of the db/db group. The left ventricular ejection fraction, left ventricular fractional shortening, E/A ratio and E/e′ ratio were not significantly different between the three groups. CFR was decreased in the db/db group, but EMPA significantly improved CFR. In contrast to the sparse and abnormal expansion of coronary microvessels observed in the db/db group, the number of coronary microvessels was increased, and the capillary diameter was decreased in the db/db + EMPA group. The number and microvascular coverage of cardiac pericytes were reduced in the db/db mice but were improved by EMPA. The cardiac fibrosis was increased in db/db group and may alleviate by EMPA.ConclusionEmpagliflozin inhibited CMD and reduced cardiac pericyte loss in diabetic mice