A Non-Iterative Balancing Method for HVAC Duct System

Building Heating, Ventilation and Air Conditioning (HVAC) system maintain comfortable indoor environment by supplying processed air to each terminal precisely through duct system. Testing, Adjusting and Balancing (TAB) plays critical role in achieving desired air distribution. Traditional TAB method is inaccurate and inefficient due to its trail-and-error natural, which forces people to pay high but expect low. Recently, it has been proposed that non-iterative approach to TAB is promising to improve performance and reduce cost. In this paper, a novel non-iterative balancing method is developed and implemented for TAB engineers to adjust dampers systematically and efficiently. Different from other TAB methods, this method is based on modeling and optimization. The mathematical model for duct system is firstly developed from its components including fan, duct segments and dampers to predict flow rates and pressures in the duct system for any damper positions. To identify the parameters in the model, flow rate measurements are taken for each terminal on real system under different damper positions. With the obtained model, optimal damper positions that gives desired air distribution are calculated by minimizing a specific objective function. To facilitate the adjusting process in real duct system, a sequential tuning instructions are generated which can help engineers to adjust dampers to their proper position using flowmeter as indicators. In this sequential tuning process, each damper only adjusts once to reach balance. Because the pressure and airflow dynamics of the duct system has been modeled, the entire TAB procedure is deterministic and non-iterative. Simulations are performed to validate the effectiveness of this method in Matlab/Simulink environment. Comparison study with existing methods shows that the proposed TAB method significantly shorten the duration of process and reduces balancing error while using easily-accessible equipment like pressure sensor and flowmeter only. It can be expected that the TAB service contractor will apply this method for advanced duct system where accurate air distribution is strictly required

Mechanical Wear Debris Feature, Detection, and Diagnosis: A Review

Mechanical debris is an important product of friction wear, which is also a crucial approach to know the running status of a machine. Many studies have been conducted on mechanical debris in related fields such as tribology, instrument, and diagnosis. This paper presents a comprehensive review of these studies, which summarizes wear mechanisms (e.g., abrasive wear, fatigue wear, and adhesive wear) and debris features (e.g., concentration (number), size, morphology, and composition), analyzes detection methods principles (e.g., offline: spectrograph and ferrograph, and online: optical method, inductive method, resistive-capacitive method, and acoustic method), reviews developments of online inductive methods, and investigates the progress of debris-based diagnosis. Finally, several notable problems are discussed for further studies. (C) 2017 Chinese Society of Aeronautics and Astronautics

The Air Distribution Around Nozzles Based On Active Chilled Beam System

During the past two decades, the utilization of Active Chilled Beam (ACB) systems as promising air-conditioning systems has becoming increasingly prevalent in Europe, North America and Asia. Due to the advantages of energy efficient, low acoustic effect and less space requirement, ACB systems are extensively applied in offices, laboratories and hospitals. However, the studies on air distribution uniformity of ACB systems are still inadequate. The air distribution has a great impact on thermal comfort. The un-uniformity of air distribution will easily lead to turbulent flow which can cause unpleased feeling such as draft in the occupied zone. ACB terminal unit is the source of the air flow entering into the occupied place, which plays a crucial role on the air distribution inside the room. Therefore, it’s of great importance to evaluate the air distribution uniformity in the vicinity of ACB nozzles. In order to fulfil the gap, air distribution for a two-way discharge ACB terminal unit is investigated in this study. The air velocities around the nozzles under different conditions are tested in a 7.3m*3.3m*2.5m thermal isolated room and simulated by a three dimensional Computational Fluid Dynamics (CFD). After being verified, the CFD model is utilized to examine the effects of nozzle diameter and inlet pressure. From the results of experiments and simulation, it is found out that the air flow is discharged in an asymmetric way from nozzles, which is ascribed to un-uniformity of pressure distribution inside ACB caused by the layout of the duct. Moreover, the un-uniformity is significant when the nozzle diameter is large and the elevation of the inlet pressure would aggravate this un-uniformity. Therefore, as we design the ACB systems, high attention on the nozzle diameter should be paid to prevent the un-uniformity air flow when the nozzles and the inlet pressure are large. Eventually, a proper strategy to solve this problem is also proposed and validated by CFD simulation.

RESA: Recurrent Feature-Shift Aggregator for Lane Detection

Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals inherent in lane annotations, lane detection task is still challenging. Thus, it is difficult for the ordinary convolutional neural network (CNN) to train in general scenes to catch subtle lane feature from the raw image. In this paper, we present a novel module named REcurrent Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary feature extraction with an ordinary CNN. RESA takes advantage of strong shape priors of lanes and captures spatial relationships of pixels across rows and columns. It shifts sliced feature map recurrently in vertical and horizontal directions and enables each pixel to gather global information. RESA can conjecture lanes accurately in challenging scenarios with weak appearance clues by aggregating sliced feature map. Moreover, we propose a Bilateral Up-Sampling Decoder that combines coarse-grained and fine-detailed features in the up-sampling stage. It can recover the low-resolution feature map into pixel-wise prediction meticulously. Our method achieves state-of-the-art results on two popular lane detection benchmarks (CULane and Tusimple). Code has been made available at: https://github.com/ZJULearning/resa

Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study

IntroductionDiabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.MethodsFor this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort (n1 = 49) and the testing cohort (n2 = 21). According to the estimated glomerular filtration rate (eGFR), patients were assigned into the normal renal function (normal-RF) group, the non-severe renal function impairment (non-sRI) group, and the severe renal function impairment (sRI) group. Based on the largest coronal image of T2WI, the speeded up robust features (SURF) algorithm was used for texture feature extraction. Analysis of variance (ANOVA) and relief and recursive feature elimination (RFE) were applied to select the important features and then support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms were used for the model construction. The values of area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis were used to assess their performance. The robust T2WI model was selected to construct a multimodal MRI model by combining the measured BOLD (blood oxygenation level-dependent) and diffusion-weighted imaging (DWI) values.ResultsThe mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort.DiscussionThe model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function

MLEE: A method for extracting object-level medical knowledge graph entities from Chinese clinical records

As a typical knowledge-intensive industry, the medical field uses knowledge graph technology to construct causal inference calculations, such as “symptom-disease”, “laboratory examination/imaging examination-disease”, and “disease-treatment method”. The continuous expansion of large electronic clinical records provides an opportunity to learn medical knowledge by machine learning. In this process, how to extract entities with a medical logic structure and how to make entity extraction more consistent with the logic of the text content in electronic clinical records are two issues that have become key in building a high-quality, medical knowledge graph. In this work, we describe a method for extracting medical entities using real Chinese clinical electronic clinical records. We define a computational architecture named MLEE to extract object-level entities with “object-attribute” dependencies. We conducted experiments based on randomly selected electronic clinical records of 1,000 patients from Shengjing Hospital of China Medical University to verify the effectiveness of the method

The comparison of manual and mechanical anastomosis after total pharyngolaryngoesophagectomy

BackgroundTotal pharyngolaryngoesophagectomy (TPLE) is considered as a curative treatment for hypopharynx cancer and cervical esophageal carcinomas (HPCECs). Traditional pharyngo-gastric anastomosis is usually performed manually, and postoperative complications are common. The aim of this study was to introduce a new technique for mechanical anastomosis and to evaluate perioperative outcomes and prognosis.MethodsFrom May 1995 to Nov 2021, a series of 75 consecutive patients who received TPLE for a pathological diagnosis of HPCECs at Sun Yat-sen Memorial Hospital were evaluated. Mechanical anastomosis was performed in 28 cases and manual anastomosis was performed in 47 cases. The data from these patients were retrospectively analyzed.ResultsThe mean age was 57.6 years, and 20% of the patients were female. The rate of anastomotic fistula and wound infection in the mechanical group were significantly lower than that in the manual group. The operation time, intraoperative blood loss and postoperative hospital stays were significantly higher in the manual group than that in the mechanical group. The R0 resection rate and the tumor characteristics were not significantly different between groups. There was no significant difference in overall survival and disease-free survival between the two groups.ConclusionThe mechanical anastomosis technology adopted by this study was shown to be a safer and more effective procedure with similar survival comparable to that of manual anastomosis for the HPCECs patients

A Facile Strategy for In Situ Core-Template-Functionalizing Siliceous Hollow Nanospheres for Guest Species Entrapment

The shell wall-functionalized siliceous hollow nanospheres (SHNs) with functional molecules represent an important class of nanocarriers for a rich range of potential applications. Herein, a self-templated approach has been developed for the synthesis of in situ functionalized SHNs, in which the biocompatible long-chain polycarboxylates (i.e., polyacrylate, polyaspartate, gelatin) provide the framework for silica precursor deposition by simply controlling chain conformation with divalent metal ions (i.e., Ca2+, Sr2+), without the intervention of any external templates. Metal ions play crucial roles in the formation of organic vesicle templates by modulating the long chains of polymers and preventing them from separation by washing process. We also show that, by in situ functionalizing the shell wall of SHNs, it is capable of entrapping nearly an eightfold quantity of vitamin Bc in comparison to the bare bulk silica nanospheres. These results confirm the feasibility of guest species entrapment in the functionalized shell wall, and SHNs are effective carriers of guest (bio-)molecules potentially for a variety of biomedical applications. By rationally choosing the functional (self-templating) molecules, this concept may represent a general strategy for the production of functionalized silica hollow structures

Area ratio effects to the performance of air-cooled ejector refrigeration cycle with R134a refrigerant

In this paper, the key ejector geometry parameters for an air-cooled ejector cycle using R134a with cooling capacity of 2 kW are designed by 1D analysis. Through enlarging the designed area ratios by connecting the replaceable nozzles with a main body, optimum area ratios under air-conditioning working conditions are studied experimentally. Three parameters, namely, the entrainment ratio, COP and cooling capacity are evaluated, and the results show that the optimum area ratios are from 3.69 to 4.76 that are lower than those mentioned in other studies. With a fixed area ratio, experiments also show that the influence of the ejector area ratio on the ejector performance largely depends on the operating conditions. Consequently, the effects of operating conditions such as primary flow pressures on the ejector system performance are evaluated