Industrial Coal Demand in China: A Provincial Analysis

The general concern on the environmental implications of the rising demand for coal registered in China during the last few years has induced considerable research effort to produce accurate forecasts of China’s energy requirements. Nevertheless, no previous study has modelled the coal demand in China at provincial level. The aim of this paper is twofold. First, we estimate and forecast the Chinese demand for coal using panel data disaggregated by provinces and accounting for spatial heterogeneity. Second, given the spatial nature of the data, we explicitly capture the spatial autocorrelation among provinces using spatial econometrics. In particular, we specify the Chinese industrial coal demand at provincial level with a fixed-effect spatial lag model and a fixed-effect spatial error model. The fixedeffect spatial lag model seems to better capture the existing interdependence between provinces. This model forecasts an average annual increase in coal demand to 2010 of 4 percent.Energy demand in China, Coal demand in China, Chinese provinces, Panel data; Spatial econometrics, Forecasting

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

Corruption and Social Interaction: Evidence from China

We explore theoretically and empirically whether social interaction, including local and global interaction, influences the incidence of corruption. We first present an interaction-based model on corruption that predicts that the level of corruption is positively associated with social interaction. Then we empirically verify the theoretical prediction using within-country evidence at the province-level in China during 1998 to 2007. Panel data evidence clearly indicates that social interaction has a statistically significantly positive effect on the corruption rate in China. Our findings, therefore, underscore the relevance of social interaction in understanding corruption.Corruption, Social Interaction, China

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

Carbon Emission Prediction and Clean Industry Transformation Based on Machine Learning: A Case Study of Sichuan Province

This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan industries using matrix normalization. DBSCAN clustering identified 16 feature classes to objectively group industries. Penalized regression models were then applied for their advantages in overfitting control, high-dimensional data processing, and feature selection - well-suited for the complex energy data. Results showed the second cluster around coal had highest emissions due to production needs. Emissions from gasoline-focused and coke-focused clusters were also significant. Based on this, emission reduction suggestions included clean coal technologies, transportation management, coal-electricity replacement in steel, and industry standardization. The research introduced unsupervised learning to objectively select factors and aimed to explore new emission reduction avenues. In summary, the study identified industry groupings, assessed emissions drivers, and proposed scientific reduction strategies to better inform decision-making using algorithms like DBSCAN and penalized regression models.Comment: 21 pages,19 figure

LED-Induced Fluorescence System for Tea Classification and Quality Assessment

A fluorescence system is developed by using several light emitting diodes (LEDs) with different wavelengths as excitation light sources. The fluorescence detection head consists of multi LED light sources and a multimode fiber for fluorescence collection, where the LEDs and the corresponding filters can be easily chosen to get appropriate excitation wavelengths for different applications. By analyzing fluorescence spectra with the principal component analysis method, the system is utilized in the classification of four types of green tea beverages and two types of black tea beverages. Qualities of the Xihu Longjing tea leaves of different grades, as well as the corresponding liquid tea samples, are studied to further investigate the ability and application of the system in the evaluation of classification/quality of tea and other foods