Search results

Maintaining a city’s identity and locality is an urgent problem in urban construction. Historical districts are very important parts of a city. However, non-heritage buildings, which make a significant contribution to the character and appearance of historical areas, are facing arbitrary demolition. They are more often ignored compared with buildings on the protection list. The paper aims to discuss these issues.

This paper chooses two historic blocks in Wuxi, in which 256 non-heritage buildings were examined, the SPSS method and the AHP method are used to find renewal modes for the buildings that had not finished updates, and the two blocks are then used as practical cases to validate the matching results.

In conclusion, on the one hand, according to different building categories, different renewal modes are found. On the other hand, this study finds differences in the renewal modes of different historic districts.

The study shows a renewal method for non-heritage buildings, which require attention due to their dangerous situation, as these buildings in historical districts also contribute to a city’s uniqueness and locality. These results should be helpful in the planning and practices for urban historical districts.

Notwithstanding its significance as a form of strategic human capital investment, employee training has not yielded consistent conclusions among scholars regarding its impact on organizational performance. Some studies deem it effective, while others regard it as ineffective. We contend that distinct types of training impact various facets of firm performance.

In this study, we categorize employee training as either exploitative or explorative. Specifically, we examine their impact on two aspects of organizational performance: short-term performance and long-term competence, using a quasi-experimental setting and a difference-in-differences (DID) method.

We find that exploitative training is more effective in improving firms’ short-term performance (e.g. firms’ sales revenue), while explorative training is more effective in enhancing firms’ long-term competence (e.g. firms’ innovation output).

The findings of this study illuminate concrete benefits of training for practitioners, suggesting that firms can strategically select employee training category to maximize returns on their investment in strategic human capital based on their strategic orientations.

This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.

An intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.

The fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.

In most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

The purpose of this paper is to introduce the realizing process of the fuzzy logical reasoning method in relation to the insulation detection of insulator strings, especially about the establishment of the fuzzy relation matrix.

Utilizing fuzzy logical reasoning method, the online fault diagnosis of the transmission line insulator strings can be realized efficiently, although the insulation status of the insulator strings is influenced by many factors and the relation between the measured electric parameters about the insulator with the degree of the deterioration is ambiguous.

Utilizing the pattern recognition method to build the fuzzy relation matrix after choosing the probability of the corona current, the root‐mean‐square value and the peak value and the pulse frequency of the leakage current as the fuzzy operating input variables, using the fuzzy reasoning method to estimate the insulation status proves to be a practical approach.

The experiments verify the correctness of this method by simulating work conditions of insulator strings on an 110 kV transmission line.

This paper proves that using the fuzzy reasoning method to estimate the insulation status of the online insulator strings is a practical approach. The results of the laboratory and the field tests verify the correctness of this approach and the validity of the identification method to build the fuzzy relation matrix.