Pneumatic components are essential for precise control and automation in mechanical manufacturing. Leakage faults in pneumatic components can seriously undermine the reliability of manufacturing processes. When high-pressure gas leaks and expands, heat transfer between the gas and the component wall creates a localized low-temperature area. This thermal anomaly can be detected using thermal imaging techniques. However, thermal images captured from different pneumatic components exhibit distinct distribution patterns, which can significantly degrade the accuracy of existing detection methods. Conventional domain adaptation methods typically use the sample mean to represent feature distributions, neglecting intraclass dispersion and the interdependence among feature learning, classifier learning, and pseudolabel learning. To address these limitations, we propose a joint unsupervised domain adaptation method via cluster centers and thermal imaging for leakage detection in different pneumatic components (JCC-LDC). Specifically, JCC-LDC accounts for the fact that samples of a single class may disperse into multiple clusters, and it integrates feature learning, classifier learning, and pseudolabel learning into a unified framework based on cluster centers. Experimental results demonstrate that JCC-LDC increases the average detection accuracy from 71.5% to 82.3%, effectively enhancing the generalization capability of thermal-imaging-based leakage detection in pneumatic components.

Signed graphs capture both positive and negative relationships between entities, with balance being a fundamental concept. In these graphs, a vertex is considered balanced if all cycles it belongs to contain an even number of negative edges. On the other hand, unbalanced vertices often experience cognitive dissonance and emotional disturbance, motivating efforts to modify the graph to achieve balance for these vertices. Yet, most existing research emphasizes global balance, focusing on lengthy cycles that represent distant interactions. In contrast, this paper shifts the focus to local balance, where a vertex is deemed balanced when the triangles (length-three cycles) it participates in are positive, reflecting more immediate relationships. Building on this, we introduce the Locally Balancing Signed Graph (LBS) problem, which aims to maximize the number of locally balanced vertices through graph modification. Despite the NP-hard nature of the LBS problem and the absence of properties such as monotonicity and submodularity, our novel greedy method effectively addresses these challenges. We further enhance our method with dynamic computation and pruning techniques. Extensive experiments show the efficacy of our greedy method in solving the LBS problem and underscore the substantial runtime reductions achieved through our optimization techniques.

Mechanical ventilation is an effective treatment for critically ill patients and those with pulmonary diseases. However, patient-ventilator asynchrony (PVA) remains a significant challenge, potentially leading to high mortality. Improving patient-ventilator synchrony poses a complex decision-making problem in clinical practice. Traditional methods rely heavily on clinicians' experience, often resulting in inefficiencies, delayed ventilator adjustments, and resource shortages. This paper proposes a novel approach using a deep reinforcement learning (RL) algorithm based on deep Q-learning (DQN) to enhance patient-ventilator synchrony during pressure support ventilation. The action space and reward function are established from clinical experience, and a pneumatic model of the mechanical ventilation system is constructed to simulate various patient conditions and types of PVAs. Clinical data are used to evaluate the RL algorithm qualitatively and quantitatively. The RL-optimized ventilation strategy reduces the proportion of breaths containing PVAs from 37.52% to 7.08%, demonstrating its effectiveness in assisting clinical decision-making, improving synchrony, and enabling intelligent ventilator control, bedside monitoring, and automatic weaning.

In order to achieve precise respiratory therapy for mechanically ventilated patients, real-time monitoring of the state parameters of inhaled and exhaled gases is required. These parameters are primarily measured by ventilators, with limitations such as insufficient monitoring parameters, circuit leaks, and constraints imposed by distance and obstacles. This paper designs a low-power wireless sensor for multi-parameter monitoring near the patient, which can be used continuously for approximately 60 days. Based on this sensor, an intelligent respiratory monitoring system with a distributed architecture is proposed to achieve intelligent patient-ventilator asynchrony (PVA) perception. Experimental results show that the system can stably and accurately collect and transmit data, with measurement errors for pressure, flow, temperature, humidity, and CO concentration being 1.3%, 2.1%, 0.6, 1% RH, 0.3 mmHg respectively. The proposed sensor and system have the potential to enhance the efficiency and intelligence of medical care significantly.

In a graph, the reverse nearest neighbors (RNN) of vertex f refer to the set of vertices that consider f as their nearest neighbor. When f represents a facility like a subway station, its RNN comprises potential users who prefer the nearest facility. In practice, there may be underutilized facilities with small RNN sizes, and relocating these facilities to expand their service can be costly or infeasible. A more cost-effective approach involves selectively upgrading some edges (e.g., reducing their weights) to expand the RNN sizes of underutilized facilities. This motivates our research on the Expanding Reverse Nearest Neighbors (ERNN) problem, which aims to maximize the RNN size of a target facility by upgrading a limited number of edges. Solving the ERNN problem allows underutilized facilities to serve more users and alleviate the burden on other facilities. Despite numerous potential applications, ERNN is hard to solve: It can be proven to be NP-hard and APX-hard, and it exhibits non-monotonic and non-submodular properties. To overcome these challenges, we propose novel greedy algorithms that improve efficiency by minimizing the number of edges that need to be processed and the cost of processing each edge. Experimental results demonstrate that the proposed algorithms achieve orders of magnitude speedup compared to the standard greedy algorithm while greatly expanding the RNN.

For patients with mechanical ventilation, secretions in airway are harmful and sometimes even mortal, it's of great significance to clear secretion timely and efficiently. In this paper, a new secretion clearance method for VCV (volume-controlled ventilation) ventilator is put forward, and a secretion clearance system with a VCV ventilator and double lungs is designed. Furthermore, the mathematical model of the secretion clearance system is built and verified via experimental study. Finally, to illustrate the influence of key parameters of respiratory system and secretion clearance system on the secretion clearance characteristics, coupling effects of two lungs on VCV secretion clearance system are studied by an orthogonal experiment, it can be obtained that rise of tidal volume adds to efficiency of secretion clearance while effect of area, compliance, and suction pressure on efficiency of secretion clearance needs further study. Rise of compliance improves bottom pressure of secretion clearance while rise of area, tidal volume, and suction pressure decreases bottom pressure of secretion clearance. This paper can be referred to in researches of secretion clearance for VCV.

Motivation: Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results: We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation: The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Secretions in the airways of mechanical ventilated patients are extremely dangerous to patients' health. In recent studies, the continuous constant airflow is adopted, however, it is not consistent with a clinical situation. To study respiratory airflow dynamic characteristics with secretion in the airways, a mathematical model based on clinical mechanical ventilation is established in this paper. To illustrate the secretion's influence on the airflow dynamics of mechanical ventilated respiratory system, three key parameters which are cross section area ratio of secretion/ pipe, air-secretion contact area, and secretion viscosity are involved in the study. Through the experimental study, the accuracy and dependability of the model are confirmed. By the simulation study, we find that: based on the model which combines two airways and two model lungs, when one of the airways was covered with secretion, the maximum pressure of the model lung which is attached to the end of this airway maintains constant when the cross section area ratio is less than 66 percent, and then it tends to decline sharply with the ratio increasing, but it remains constant with the augment of air-secretion contact area, the maximum flow declines both with the increasing of cross section area ratio and air-secretion contact area. Furthermore, as for the other airway, the maximum pressure of the model lung has no significant changes with the augment of area ratio and air-secretion contact area, however, along with the increasing of area ratio and air-secretion contact area, the maximum flow rises up. Moreover, the secretion viscosity has barely any influence on airflow dynamics. According to our analysis results, we conclude that the cross section area ratio of secretion/pipe has bigger influence on airflow dynamic characteristics than air-secretion contact area and secretion viscosity. This paper lays the foundation for the further study of efficacy and safety in mechanical ventilation and the secretion clearance of mechanical ventilated patients. In addition, the mathematical model proposed in this paper can also be referred to study on the secretion movement in human airways.

Mechanical ventilation is an important method to help people breathe. Respiratory parameters of ventilated patients are usually tracked for pulmonary diagnostics and respiratory treatment assessment. In this paper, to improve the estimation accuracy of respiratory parameters, a pneumatic model for mechanical ventilation was proposed. Furthermore, based on the mathematical model, a recursive least-squares algorithm was adopted to estimate the respiratory parameters. Finally, through experimental and numerical study, it was demonstrated that the proposed estimation method was effective and the method can be used in pulmonary diagnostics and treatment.

In this paper, the main form of energy consumption of typical compressed air system is introduced, to analyze basic potentials of energy saving. Then the current situation of energy saving in compressed air systems is also given. Four major energy saving measures according to present problems of energy saving in compressed air systems and prospects of energy saving of air system are proposed.

Energy-saving of compressed air system was very important for the sustainable development of enterprises, which could be achieved though fast and accurate load forecasting. In this paper, according to the distribution rules and characteristics of 24 hours compressed air supply, the 24h compressed air flow demand model was firstly built with least square support vector machine (LSSVM). In order to avoid the long time consumption for determining the model parameters in the traditional cross validation method, Bayesian evidence framework was selected to train the parameters, and then identified and optimized them. Meanwhile, Nyström low- rank approximation decomposition algorithm was used to accelerate kernel matrix decomposition process. Though the experimental verification with real industrial data, the modeling time of LSSVM within Bayesian evidence framework is reduced to 1/20 compared with traditional cross-validation method; in the contrast with Practical Swarm Optimization (PSO), the modeling time is reduced to 80%, and the prediction accuracy can increase 14.3%, proving this method quite suitable for fast and accurate forecasting for large flow compressed air load.

Pneumatic system operation status and problems were analyzed in the industrial production site; Considering the 24 hours as a time unit, the feature of compressed air consumption was studied every 20 seconds in 24 hours in the industrial production site. Then, presenting a forecasting method, which is workable and the overall mean in line with air consumption changes in the law. This forecasting method is for the purpose of the operation dispatching of air compressors in the industrial site, and the forecasting accuracy meets the requirement of air compressors operation dispatching. Finally, with the forecasting consumption of compressed air, a optimal scheduling algorithm of compressor group is given.

Miner enterprises are often equipped with general-purpose machine tools, which are not in stream-line and hard to control by network. The paper puts forward an information management system to manage the work site automatically. With networking technology, which integrating the agreement of TCP/IP and 485, the information of the tools can be gathered automatically and the tools can be controlled effectively; With the wireless remote controlled intelligent trolley, which can get, send and put articles in the delimited zone, the conversion cycle is shorten greatly; All the tools are controlled completely, so the managing is more easy, the efficiency and the quality are improved greatly; because of the shorten of the turnover time, the worker's working efficiency is improved too.