A Clustering-based Method for Business Hall Efficiency Analysis

Excessive or insufficient business hall resources may result in unreasonable resource allocation, adversely affecting the value of an entity business hall. Therefore, proper characteristic parameters are the key factors for analyzing the business hall, which strongly affect the final analysis results. In this study, a characteristic analysis method for the economic operation of a business hall is developed and the feature engineering is established. Because of its simplicity and versatility, the k -means algorithm has been widely used since it was first proposed around 50 years ago. However, the classical k -means algorithm has poor stability and accuracy. In particular, it is difficult to achieve a suitable balance between of the centroid initialization and the clustering number k . We propose a new initialization (LSH- k -means) algorithm for k -means clustering. This algorithms is mainly based on locality-sensitive hashing (LSH) as an index for computing the initial cluster centroids, and it reduces the range of the clustering number. Furthermore, an empirical study is conducted. According to the load intensity and time change of the business hall, an index system reflecting the optimization analysis of the business hall is established, and the LSH- k -means algorithm is used to analyze the economic operation of the business hall. The results of the empirical study show that the LSH- k -means that the clustering method outperforms the direct prediction method, provides expected analysis results as well as decision optimization recommendations for the business hall, and serves as a basis for the optimal layout of the business hall.

Download Full-text

A New Decision Optimization Prediction Method Based on Short-Term Time Series and Kalman Filter

Software Engineering and Applications ◽

10.12677/sea.2018.75028 ◽

2018 ◽

Vol 07 (05) ◽

pp. 243-250

Author(s):

静陈

Keyword(s):

Time Series ◽

Kalman Filter ◽

Prediction Method ◽

Short Term ◽

Decision Optimization

Download Full-text

A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators

Sensors ◽

10.3390/s19092055 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2055 ◽

Cited By ~ 7

Author(s):

Kai-Bo Zhou ◽

Jian-Yu Zhang ◽

Yahui Shan ◽

Ming-Feng Ge ◽

Zi-Yue Ge ◽

...

Keyword(s):

Prediction Method ◽

Vibration Signal ◽

Strong Stability ◽

Prediction Methods ◽

Multi Objective Optimization ◽

Time Data ◽

Multi Objective ◽

Empirical Wavelet Transform ◽

Learning Machine ◽

Stability And Accuracy

The hydropower generator unit (HGU) is a vital piece of equipment for frequency and peaking modulation in the power grid. Its vibration signal contains a wealth of information and status characteristics. Therefore, it is important to predict the vibration tendency of HGUs using collected real-time data, and achieve predictive maintenance as well. In previous studies, most prediction methods have only focused on enhancing the stability or accuracy. However, it is insufficient to consider only one criterion (stability or accuracy) in vibration tendency prediction. In this paper, an intelligence vibration tendency prediction method is proposed to simultaneously achieve strong stability and high accuracy, where vibration signal preprocessing, feature selection and prediction methods are integrated in a multi-objective optimization framework. Firstly, raw sensor signals are decomposed into several modes by empirical wavelet transform (EWT). Subsequently, the refactored modes can be obtained by the sample entropy-based reconstruction strategy. Then, important input features are selected using the Gram-Schmidt orthogonal (GSO) process. Later, the refactored modes are predicted through kernel extreme learning machine (KELM). Finally, the parameters of GSO and KELM are synchronously optimized by the multi-objective salp swarm algorithm. A case study and analysis of the mixed-flow HGU data in China was conducted, and the results show that the proposed model performs better in terms of predicting stability and accuracy.

Download Full-text

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Complexity ◽

10.1155/2020/4346803 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Xue-Bo Jin ◽

Hong-Xing Wang ◽

Xiao-Yi Wang ◽

Yu-Ting Bai ◽

Ting-Li Su ◽

...

Keyword(s):

Power System ◽

Prediction Method ◽

Bayesian Optimization ◽

Load Prediction ◽

Economic Operation ◽

Power Load ◽

Decomposition Structure ◽

Optimization Parameters ◽

Sustainable Power ◽

Gated Recurrent Unit

The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.

Download Full-text

Association between blood β-hydroxybutyrate at 7 days postpartum and milk yield, disease occurrence and fertility in grazing dairy cattle with seasonal calving: a case study

Animal Production Science ◽

10.1071/an19414 ◽

2020 ◽

Vol 60 (14) ◽

pp. 1737

Author(s):

Pedro Melendez ◽

Kaitlin McDaniel ◽

Carlos Chacon ◽

Scott Poock ◽

Julian Bartolome ◽

...

Keyword(s):

Pregnancy Rate ◽

Milk Yield ◽

Management Strategies ◽

Significant Proportion ◽

Dairy Herd ◽

Final Analysis ◽

Southern Chile ◽

Characteristic Analysis ◽

Disease Occurrence

Context Ketosis in grazing cattle has been sparsely studied. A large commercial grazing dairy in southern Chile, representative of a significant proportion of the systems in the country, was used in this case study. Aims The study had three objectives: (i) to establish a cut-off for β-hydroxybutyrate (BHB) concentration for subclinical ketosis (SCK), and use this to measure the proportion of cows with SCK at 7 days postpartum in spring- and autumn-calving cows; (ii) to describe the relationship of SCK and other periparturient diseases and fertility; and (iii) to compare milk yield of healthy cows and those affected by SCK in a dairy herd with autumn and spring parturitions under grazing conditions in southern Chile. Methods During 2016, 234 cows with autumn parturitions and 632 cows with spring parturitions (n = 866) were assessed for blood BHB at 7 days postpartum. A receiver operating characteristic analysis for a BHB cut-off value was completed. Models were developed for disease occurrence, culling risk, conception risk and pregnancy rate, considering SCK as the main explanatory variable. Key results In total, 810 cows were used for the final analysis. The frequency of cows with SCK, based on the cut-off value obtained (BHB ≥1.1 mmol/L), was 22.2% at 7 days postpartum. The risk of SCK was higher (P < 0.0001) in cows calving in spring (27.0%) than in autumn (10.3%), and in multiparous (24.6%) than primiparous cows (15.1%). The seasonal difference in proportion of cows with SCK was parity-dependent, because the frequency of SCK in multiparous cows was higher (P < 0.0005) in spring (32.0%) than autumn (10.1%), whereas SCK in primiparous cows showed no significant (P = 0.41) difference between spring (15.4%) and autumn (12.5%). Milk production up to 100 days-in-milk was greater (P = 0.002) in cows with SCK (3394 kg) than without SCK (3015 kg). Disease occurrence was higher (P < 0.0001) in cows with SCK and in multiparous cows (P < 0.0001). There was no difference in conception risk at first service (P = 0.62) or in overall pregnancy rate (P = 0.90) between cows with and without SCK. Conclusions Multiparous cows calving in spring had the highest risk of SCK (BHB ≥1.1 mmol/L). SCK was associated with higher milk yield and greater occurrence of other diseases, but not with reproductive performance. Implications Grazing herds have challenges with SCK that may require different management strategies depending on the calving season and the parity of the animals.

Download Full-text

Prediction method and empirical study of pharmaceutical industry technology innovation based on TRIZ theory

2010 International Conference on Management Science & Engineering 17th Annual Conference Proceedings ◽

10.1109/icmse.2010.5719802 ◽

2010 ◽

Author(s):

Lin Yan ◽

Wang Hong-qi ◽

Wang Wan

Keyword(s):

Pharmaceutical Industry ◽

Empirical Study ◽

Technology Innovation ◽

Prediction Method

Download Full-text

Time to Catheter Angiography for Gastrointestinal Bleeding after Prior Positive Investigation Does Not Affect Bleed Identification

Journal of Clinical Imaging Science ◽

10.25259/jcis_132_2019 ◽

2020 ◽

Vol 10 ◽

pp. 16

Author(s):

Akshaar Brahmbhatt ◽

Pranay Rao ◽

Andrew Cantos ◽

Devang Butani

Keyword(s):

Computed Tomography ◽

Gastrointestinal Bleeding ◽

Final Analysis ◽

International Normalized Ratio ◽

Catheter Angiography ◽

Characteristic Analysis ◽

Coagulation Therapy ◽

Computed Tomography Angiogram ◽

Prior Investigation ◽

Packed Rbcs

Objective: To determine, time to angiography for patients with positive gastrointestinal bleeding (GIB) on prior investigation (endoscopy [ES], nuclear medicine [NM] Tc99m red blood cells (RBC) scan, or computed tomography angiography), affects angiographic bleed identification. Materials and Methods: Visceral Angiograms performed from January 2012 to August 2017 were evaluated. Initial angiograms performed for GIB were included in the study. Exclusion criteria included recent abdominal surgery or procedure (30 days), empiric embolization (embolization without visualized active bleeding), and use of vasodilators, or subsequent angiogram. Timing and results of ES, NM Tc99m RBC scan, or computed tomography angiogram and catheter angiogram were recorded. In addition, age, gender, angiogram time, anti- platelet therapy, anti-coagulation therapy, bleed location, international normalized ratio, and units of packed RBCs received in the 24 h before catheter angiography were included in the study. Results: One hundred and seventy angiograms were included in the final analysis. Forty-three angiograms resulted in the identification of an active bleed (68.9 years, and 67.4% male). All of these patients were embolized successfully. One hundred and twenty-seven angiograms failed to identify an active bleed (70.4 years, and 49.6% male). No significance was found across the two groups with respect to time from prior positive investigation. Receiver operating characteristic analysis demonstrated that units of packed RBCs received in the preceding 24 h were correlated with positive bleed identification on catheter angiography. Conclusion: Time to angiography from prior positive investigation, including ES, NM Tc99m RBC scan, or computed tomography angiogram does not correlate with positive angiographic outcomes. Increasing units of packed RBCs administered in the 24 h before angiogram do correlate with positive angiographic findings.

Download Full-text