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Predicting electrical power consumption is essential in many industries. This paper presents a method to predict total electrical power consumption in a certain period of Hot Rolling Mill (HRM) in steel mills. We adequately pre-processed operation data, including work schedule, work result, and power measurement. In addition, based on domain knowledge, we derived several useful variables relevant to power consumption. To predict power consumption, we developed a Multi-Level Stacked Regression model that combines the strengths of multiple prediction models. Real-world applications have dataset shift characteristics in which the covariance between features or distribution of the features changes over time due to frequent changes in the working environment. To verify the generalization accuracy of our model, we trained and tested our model for various training/test splits and compared it with several comparative models. Our model showed the best accuracy in all training/test splits. We verified whether the predictive model reflects the real-world environment well by analyzing the actual physical meaning of the essential features used in the predictive model. • A series of pre-processing methods are applied to real data from Hot Rolling Mill. • A Multi-Level Stacked Regression algorithm is proposed. • Performance on various training/test splits that contains dataset shifts is verified. [ABSTRACT FROM AUTHOR]

• A weakly supervised power line detection algorithm is proposed. • A line specialized refinement algorithm is applied for performance improvement. • A repeated learning framework with noise label develops the model output. Detection of power lines in aerial images is an important problem to prevent accidents of unmanned aerial vehicles operating at low altitudes in the electrical industry. Recently, pixel-level power line detection using deep learning has been studied but production of the pixel-level annotations for massive dataset is difficult. In this study, we propose a power line detection algorithm using weakly supervised learning method to reduce the labeling cost for dataset generation. The algorithm is divided into two stages. First, an approximately localized mask was generated based on a convolutional neural network which was trained with only patch-level labels. Second, recursive training of segmentation network with refined broken line segments was executed. A refinement algorithm, line segment connecting (LSC) is a power-line-specialized refinement module that connects broken lines by approximating the segments as partially straight. In proposed algorithm, predicted image at each recursive step was updated as a label of the next training and the label was developed by itself with LSC. The comprehensive experimental results of our algorithm showed state-of-art F 1 -score of 94.3% in weakly supervised learning approaches on public dataset. This result suggests that the proposed algorithm is useful for low labeling cost with high performance in line detection application. [ABSTRACT FROM AUTHOR]

By using a MoI 2 precursor, a one-dimensional linear chain material, Mo 6 S 3 I 6 , was synthesized on a centimeter scale. As MoI 2 can be used to form Mo 6 S 3 I 6 through a gas-phase reaction, a larger amount of single crystals can be synthesized; moreover, these crystals are significantly longer than those synthesized by using Mo, S, and I powder. The synthesized Mo 6 S 3 I 6 exhibited the characteristics of paramagnetic semiconductors with excellent thermal stability and a low bandgap. Furthermore, Mo 6 S 3 I 6 can be separated into long nanometer-scale chains by a solution dispersion process. • First synthesis of cm scale synthesize of vdW 1D material Mo 6 S 3 I 6. • Superior material properties based on high crystallinity. • Atomic level (nm) dispersion for ideal 1D materials. [ABSTRACT FROM AUTHOR]

A new of 2D transition metal thiophosphates, Nb 4 P 2 S 21 , was synthesized through a vapor transport reaction. High purity and centimeter-scale large Nb 4 P 2 S 21 crystals were obtained by controlling the growth temperature and stoichiometric ratio between niobium, phosphorus, and sulfur (using excess sulfur condition, Nb: P: S = 4: 2: 23). It was confirmed that large Nb 4 P 2 S 21 crystals could be separated to a nanometer scale through mechanical exfoliation, and the exfoliated Nb 4 P 2 S 21 layer exhibits semiconducting characteristics by measuring the change in electrical conductivity with temperature (increased conductivity with temperature in the range of 93–333 K). Furthermore, the device characteristics for detecting visible ray laser (635 nm) and NO 2 gas molecules were verified. Based on these characteristics, the synthesized 2D material is expected to be a potential candidate for additional 2D materials and in multi-functional sensor devices, including optoelectronics and gas sensing applications. • Synthesis of 2D transition metal thiophosphates Nb 4 P 2 S 21. • Bulk to few nanometer scale separation of Nb 4 P 2 S 21 using mechanical exfoliation. • Multi-functional sensing application in photodetector and gas sensor. [ABSTRACT FROM AUTHOR]

A new type of 1D inorganic material, MoI 3 , which can be separated into individual 1D chains, was synthesized through a vapor transport reaction between molybdenum and iodine. High-purity and centimeter-scale large MoI 3 crystals were obtained by controlling the growth temperature and the stoichiometric ratio between molybdenum and iodine. Their semiconductor characteristics were confirmed by measuring the electrical conductivity as a function of temperature (conductivity increased with increased temperature across the range −70 to 30 °C). Furthermore, it was confirmed that MoI 3 with a 1.27 eV band gap can be separated to a few nanometer scale through mechanical and chemical exfoliation methods and can be used as a photodetector in the near IR (less than 1000 nm)-to-visible light region. This novel 1D material is expected to play an important role in the development of additional low-dimensional materials and in advanced nano-optoelectronic devices. • Synthesis of 1D inorganic material MoI 3 via chemical vapor transport (CVT) method. • Experimental estimation of band gap (∼ 1.27 eV) and application to photodetector. • Bulk to few nanometer scale separations of MoI 3 chains using mechanical and chemical exfoliation. [ABSTRACT FROM AUTHOR]

The two-dimensional (2D) inorganic material Nb 3 I 8 , which can be separated into atomic planes, was synthesized through a vapor transport reaction between niobium and iodine. High-purity and cm scale large Nb 3 I 8 crystals were obtained by controlling the growth temperature (600–700 °C) and the stoichiometric ratio between niobium and iodine (using excess I 2 condition, niobium: iodine = 3:9). The semiconductor characteristics were measured by the change of electrical conductivity with temperature (increased conductivity with temperature in the range of 100 K–300 K), and have a band gap of ∼1 eV (∼1200 nm) through Infrared (IR)-visible light absorption analysis. Based on these characteristics, the device characteristics of detecting IR near 1200 nm region were verified. Besides few-layers of Nb 3 I 8 was acquired by the mechanical exfoliation method, and experimentally observed by atomic force microscopy (AFM). This 2D material is expected to have an important role in developing another 2D materials and in new nano-optoelectronic devices using it. • Synthesis of 2D material Nb 3 I 8 in centimeter scale. • Experimental estimation of band gap of Nb 3 I 8 about (∼1 eV) and application to IR detectors. • Bulk to few-layers separation of Nb 3 I 8 sheet using mechanical exfoliation method. [ABSTRACT FROM AUTHOR]