Gotowa bibliografia na temat „Abnormal value removing”

Dust-removing blower is a key equipment in sintering plants, which can provide enough wind and negative pressure. It can also improve the efficiency of dust-removing. The vibration level of a dust-removing blower in a sintering plant is very high, which is beyond its normal value. Due to the complex working condition and strong background noise, it is difficult to extract fault features from the vibration signal of the dust-removing blower. Therefore, fault analysis of the blower is very difficult. Since the modulation phenomenon existed in the vibration signal of the blower is found, the envelope analysis based on the Hilbert transform is proposed to demodulate the vibration signal. The frequency spectrum of the demodulated signal shows that the first order frequency characteristic is obvious, which can effectively reveal the dynamic unbalance of the rotor system is the main reason of the abnormal vibration of the blower. According to this diagnosis, some possible reasons for the unbalance are proposed, as well as advices regarding to the repair of the blower system. Moreover, the test and analysis are conducted on the repaired blower system. The results show that the unbalance problem is eliminated and the blower can work normally, which can validate the accuracy and reliability of the proposed diagnosis method for fault analysis of the dust-removing blower.Keywords: dynamic unbalance; modulation; dust-removing blower; Hilbert Transform

Climate change poses higher requirements on ocean engineering design, and reasonable estimation of design wave heights plays a crucial role in coastal protection and offshore engineering. Extreme value analysis is widely used in frequency calculations of wave parameters, among which the peak over threshold method based on the generalized Pareto distribution is proven to be an effective method, and the different selection of extreme wave samples in this method has a great influence on the calculation results. In this study, long-term significant wave height series were utilized to investigate the long-range correlation of significant wave heights, and thresholds were determined based on the changes of long-range correlations. This approach assumes that extreme events and non-extreme events are generally caused by different physical processes, where extreme events result from massive disturbances leading to abnormal states, and long-range correlations are not affected or minimally affected by extreme events. Thus, thresholds can be determined based on changes of long-range correlations by removing extreme events. Comparing this method to graphical diagnostic techniques, we demonstrated its rationality in determining extreme wave height thresholds. Moreover, the automatic threshold selection offered by this method helps to mitigate errors associated with subjective judgments in traditional approaches.

This study examines whether the stock market returns surrounding announcements of mergers and acquisitions (M&A) are higher for acquiring firms audited by industry specialists. External auditors are uniquely positioned to provide assurance on the financial statements of their acquiring clients both before and after an acquisition. Also, an important aspect of due diligence in M&A transactions is the external auditors review of the accounting records, financial statements, internal controls and information systems of the target company. Using a sample of 4,283 M&A announcements between 1988 and 2011 in the United States of America, we report the results from our main regressions, controlling for all the bidder traits and deal characteristics. We examine incremental effect of audit firm specialization on cumulative abnormal returns. We also measure the effect of audit firm industry specialization in a reduced sample of 3,946 acquisitions after removing all non-Big N auditors. We use Heckmans (1979) two-step procedure to ensure that announcement period return to the size of the audit firm is not driven by the determinants related to auditor choice. Consistent with the idea that industry specialists provide higher quality assurance and possibly superior M&A advisory services, we find that the stock market returns are higher when acquiring firms are audited by industry specialists.

The conducted laboratory tests allowed determination of the efficiency of removing ammonium nitrogen, iron, and manganese in the biofiltration process on chalcedonite beds. The process of water purification was carried out by a single- and two-stage biofiltration method with gravitational and anti-gravitational flow. The study examined the extent to which chemical activation of the bed with potassium manganese (VII) affects the course of the nitrification process and the rate of biofilm formation. The obtained test results indicate that two-stage biofiltration, with initial chemical activation at the first stage of biofiltration, is an effective method for purifying waters with an abnormal content of ammonium nitrogen with simultaneous removal of iron and manganese. Activation of the bed had an effect on, among other things: biofilm formation time, efficiency of removing manganese (II) ions, and oxygen consumption in the biofiltration process. Due to the longer maturation time of the activated bed, the normative value of ammonium nitrogen (< 0.39 N-NH4+) was obtained on the 23rd day of the operation of the filters, and in the non-activated bed on the 14th day. The method of bed preparation did not affect the efficiency of removal of iron compounds.

This study by combining data cleaning, clustering analysis and signal timing method, Webster to famous scenic town traffic control provides a scientific and efficient solution. By removing the missing license plate numbers and abnormal data in the open source data set, the processed data were visualized and analyzed. The cluster analysis was used to divide the peak period of traffic flow, the statistics of the traffic flow period data, the Webster method was used to optimize the timing of traffic lights, and the service level model was established to analyze the results. It was found that it could effectively improve the traffic efficiency. Ease traffic congestion. This research is not only of great significance in theory, but also has application value in actual traffic management, showing its innovation and practical value. The future research of algorithms and to further explore the more advanced technology, to achieve more accurate and efficient traffic flow control.

Background Differentiating between true and pseudohyperkalaemia is essential for patient management. The common causes of pseudohyperkalaemia include haemolysis, blood cell dyscrasias and EDTA contamination. One approach to differentiate between them is by checking the renal function, as it is believed that true hyperkalaemia is rare with normal function. This is logical, but there is limited published evidence to support it. The aim of this study was to investigate the potential role of the estimated glomerular filtration rate in differentiating true from pseudohyperkalaemia. Methods GP serum potassium results >6.0 mmol/L from 1 January 2017 to 31 December 2017, with a repeat within seven days, were included. Entries were retrospectively classified as true or pseudohyperkalaemia based on the potassium reference change value and reference interval. If the initial sample had a full blood count, it was classified as normal/abnormal to remove blood cell dyscrasias. Different estimated glomerular filtration rate cut-points were used to determine the potential in differentiating true from pseudohyperkalaemia. Results A total of 272 patients were included with potassium results >6.0 mmol/L, with 145 classified as pseudohyperkalaemia. At an estimated glomerular filtration rate of 90 ml/min/1.73 m2, the negative predictive value was 81% (95% CI: 67–90%); this increased to 86% (95% CI: 66–95%) by removing patients with abnormal full blood counts. When only patients with an initial potassium ≥6.5 mmol/L were included (regardless of full blood count), at an estimated glomerular filtration rate of 90 ml/min/1.73 m2, the negative predictive value was 100%. Lower negative predictive values were seen with decreasing estimated glomerular filtration rate cut-points. Conclusion Normal renal function was not associated with true hyperkalaemia, making the estimated glomerular filtration rate a useful tool in predicting true from pseudohyperkalaemia, especially for potassium results ≥6.5 mmol/L.

To improve the accuracy of detecting soil total nitrogen (STN) content by an artificial olfactory system, this paper proposes a multi-feature optimization method for soil total nitrogen content based on an artificial olfactory system. Ten different metal–oxide semiconductor gas sensors were selected to form a sensor array to collect soil gas and generate response curves. Additionally, six features such as the response area, maximum value, average differential coefficient, standard deviation value, average value, and 15th-second transient value of each sensor response curve were extracted to construct an artificial olfactory feature space (10 × 6). Moreover, the relationship between feature space and soil total nitrogen content was used to establish backpropagation neural network (BPNN), extreme learning machine (ELM), and partial least squares regression (PLSR) models were used, and the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were selected as prediction performance indicators. The Monte Carlo cross-validation (MCCV) and K-means improved leave-one-out cross-validation (K-means LOOCV) were adopted to identify and remove abnormal samples in the feature space and establish the BPNN model, respectively. There were significant improvements before and after comparing the two rejection methods, among which the MCCV rejection method was superior, where values for R2, RMSE, and RPD were 0.75671, 0.33517, and 1.7938, respectively. After removing the abnormal samples, the soil samples were then subjected to feature-optimized dimensionality reduction using principal component analysis (PCA) and genetic algorithm-based optimization backpropagation neural network (GA-BP). The test results showed that after feature optimization the model indicators performed better than those of the unoptimized model, and the PLSR model with GA-BP for feature optimization had the best prediction effect, with an R2 value of 0.93848, RPD value of 3.5666, and RMSE value of 0.16857 in the test set. R2 and RPD values improved by 14.01% and 50.60%, respectively, compared with those before optimization, and RMSE value decreased by 45.16%, which effectively improved the accuracy of the artificial olfactory system in detecting soil total nitrogen content and could achieve more accurate quantitative prediction of soil total nitrogen content.

The scale of Software Defined Networks (SDN) is expanding rapidly and the demands for security reinforcement are increasing. SDN creates new targets for potential security threats such as the SDN controller and networking devices in the data plane. Violation of data plane integrity might lead to abnormal behaviors of the overall network. In this paper, we propose a new security approach for OpenFlow-based SDN in order to detect violation of switches flow tables integrity and successfully locate the compromised switches online. We cover all aspects of integrity violation including flow rule adding, modifying and removing by an unauthorized entity. We achieve this by using the cookie field in the OpenFlow protocol to put in a suitable digest (hash) value for each flow entry. Moreover, we optimize our method performance by calculating a global digest value for the entire switch’s flow table that decides whether a switch is suspected of being compromised. Our method is also able to determine and handle false alarms that affect the coherence of a corresponding table digest. The implementation is a reactive java module integrated with the Floodlight controller. In addition, we introduce a performance evaluation for three different SDN topologies.

Supplier concentration (SUC) has disadvantage of vulnerability along with cost savings and efficiency. While current scholarship focus on the vulnerability of firms with centralized suppliers during the COVID-19 epidemic, there is no empirical study that explores the impact of post-disaster SUC on firm value as countries removing regional isolation policy. I focus on the impact of COVID-19 reopening policy on investor attitudes towards SUC after the resolution of a supply chain disruption crisis. I try to examine whether investors still perceive SUC as a risk signal or as a positive signal for rapid recovery. Using the event shock of China’s reopening announcement and data on A-share listed companies, I find that SUC has a positive impact on cumulative abnormal returns at reopening. I also find that positive effect of SUC is more prominent for firms that benefit from a larger reduction in transaction costs due to the reopening policy. I also analyze the moderating effect and find that information intermediaries such as analysts and media attention amplify the positive effects of SUC. My research provides new perspective on achieving post-disaster value enhancement through SUC.

Existing distance-based outlier mining methods do not consider the impact of each attribute's importance degree, thereby resulting in poor mining accuracies. To address this problem, we propose a new outlier mining algorithm – Miner* – that makes use of information entropy and Weighted Distance Sum to substantially improve mining accuracies. Miner* employs information entropy to determine weight values indicating the importance degrees of data attributes. An input dataset is reduced by Miner* through the neighbour-radius-based pruning technologies. Thus, Miner* obtains a candidate outlier set by removing any data objects that are unlikely to be outliers. Miner* calculates the weighted distance sum value Wkof each object in the candidate outlier set; Wkvalue ranks the top n to be regarded as outliers. Due to the sum of distance, which takes full advantage of the clustering characteristics of the dataset, edge distribution data objects and local outliers can be effectively mined out. To demonstrate the effectiveness of the Miner* algorithm, we implement Miner* in a prototype system to detect star spectrum data objects with abnormal characteristic lines. Our experimental results show that the algorithm in Miner* achieves high accuracy, high scalability, and low man-made influence by utilizing UCI and star spectrum dataset. Our results also confirm that Miner* is feasible and effective in mining spectrum data with abnormal characteristic lines from massive star spectrum dataset.