Export Competition in China: Evidence from Data at Provincial Level

18 Halama

Declining Labor Share: Is China's Case Different?

This paper explores why labor share in China has declined since the middle of the 1990s. Existing literature usually ascribes the labor share decline in developed countries to biased technological progress. However, our investigation shows that China's case is different. Using a simultaneous equation model estimated with three-stage least squares, we find that FDI, levels of economic development and privatization have negative effects on the labor share. The negative influence of FDI on labor share results from regional competition for FDI, which weakens labor forces' bargaining power. A U-shaped relationship exists between labor share and the level of economic development, and China is now on the declining part of the curve. The negative effects of privatization on the labor share stem from the elimination of the so-called "wage costs eroding profit" situation and the positive supply shock on the labor market. Copyright (c) 2010 The Authors China & World Economy (c) 2010 Institute of World Economics and Politics, Chinese Academy of Social Sciences.

Transient signal identification of HVDC transmission lines based on wavelet entropy and SVM

High-voltage DC (HVDC) transmission plays an important role in power transmission projects due to its advantages of large transmission power and good control performance. As the main protection of the DC transmission line, transient protection uses the high-frequency signal generated by fault transient to detect faults, having the characteristics of fast response and high accuracy. However, the HVDC transmission line has complex conditions along the route and is vulnerable to lightning strikes and other accidents, resulting in the occurrence of a variety of transients in the line, which increases the difficulty of fault identification. Being able to reveal signal time-frequency characteristic, wavelet entropy is an effective tool of signal recognition. This study proposes a method of transient signal identification based on the wavelet entropy and support vector machine (SVM). Firstly, the transient processes of three kinds of signals, including unipolar faults, lightning strike faults, and lightning disturbances, are briefly introduced. Then the time−frequency features of three kinds of transient signals under different scenes are analysed by wavelet entropy. Finally, the training set was used to train the SVM classification model with the signal wavelet entropy being taken as the eigenvector, and the test results validate the effectiveness of the proposed method

EU-China Trade and intra-EU Trade: Substitute or Complementary?

This paper examines how EU-China trade affected intra-EU trade. The estimation shows that when a country's share of trade with China increased, its share of trade with EU partners declined. This suggests that stronger trade links with China resulted in weaker trade links among EU countries. Furthermore, the "disintegration" effect of the export to China was stronger than that of import from China, meaning that the influence of China as an export destination was greater than that of China as a source of import. An extended analysis shows that the disintegration effect was most strongly felt in trade links among EU core countries, less strongly felt in trade links between EU core and periphery countries, and least strongly felt in trade links among EU periphery countries. In comparison, we find that EU import from the US and India significantly weakened and strengthened intra-EU trade respectively. Estimation results using product level data demonstrate that the effects depend on the types of products we are concerned with. Whether using gross value or value added, the conclusions remain valid

EU-China Trade and intra-EU Trade: Substitute or Complementary?

This paper examines how EU-China trade affected intra-EU trade. The estimation shows that when a country's share of trade with China increased, its share of trade with EU partners declined. This suggests that stronger trade links with China resulted in weaker trade links among EU countries. Furthermore, the "disintegration" effect of the export to China was stronger than that of import from China, meaning that the influence of China as an export destination was greater than that of China as a source of import. An extended analysis shows that the disintegration effect was most strongly felt in trade links among EU core countries, less strongly felt in trade links between EU core and periphery countries, and least strongly felt in trade links among EU periphery countries. In comparison, we find that EU import from the US and India significantly weakened and strengthened intra-EU trade respectively. Estimation results using product level data demonstrate that the effects depend on the types of products we are concerned with. Whether using gross value or value added, the conclusions remain valid

Entropy SVM–Based Recognition of Transient Surges in HVDC Transmissions

Protection based on transient information is the primary protection of high voltage direct current (HVDC) transmission systems. As a major part of protection function, accurate identification of transient surges is quite crucial to ensure the performance and accuracy of protection algorithms. Recognition of transient surges in an HVDC system faces two challenges: signal distortion and small number of samples. Entropy, which is stable in representing frequency distribution features, and support vector machine (SVM), which is good at dealing with samples with limited numbers, are adopted and combined in this paper to solve the transient recognition problems. Three commonly detected transient surges—single-pole-to-ground fault (GF), lightning fault (LF), and lightning disturbance (LD)—are simulated in various scenarios and recognized with the proposed method. The proposed method is proved to be effective in both feature extraction and type classification and shows great potential in protection applications

Deep learning-based fault location of DC distribution networks

Compared with AC distribution networks, DC ones have a number of advantages. Intensive connections of distributed renewable energy can lead to large amount of power electronic converters and complex models. Underground cable is widely used in DC distribution networks. Accurate location of faults can help engineers find the fault points and shorten the time of maintenance. In DC distribution networks, where only a few measuring units are equipped and low sampling rates are adopted, there is limited data used for fault location. Also, for monopole grounding fault, the fault features are sometimes unobvious for recognition. Deep learning which provides feature hierarchy can learn experiences automatically and recognise raw data as human brain does. It reveals a high potential to solve location problems in DC distribution systems. This paper proposes a depth learning based fault location for DC distribution networks. First, a DC distribution network with radiant topology is modelled, and faults are added with different parameters to simulate various scenarios in practical projects. Then, a deep neural network is generated and trained with normalised fault currents. The parameters of network are discussed according to particular application. Finally, the location performance of deep neural network is tested

Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology

This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection

Independent component analysis based digital signal processing in coherent optical fiber communication systems

In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed