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        검색결과 66

        1.
        2024.02 KCI 등재 구독 인증기관·개인회원 무료
        Plants synthesize antioxidant compounds as a defense mechanism against reactive oxygen species. Recently, plant-derived antioxidant compounds have attracted attention due to the increasing consumer awareness in the heath industry. However, traditional methods for measuring the antioxidant activity of these compounds are time-consuming and costly. Therefore, our study constructed a quantitative structure-activity relationship (QSAR) model that can predict antioxidant activity using graph convolutional networks (GCN) from plant structural data. The accuracy (Acc) of the model reached 0.6 and the loss reached 0.03. Although with lower accuracy than previously reported QSAR models, our model showed the possibility of predicting DPPH antioxidant activity in a wide range of plant compounds (phenolics, polyphenols, vitamins, etc.) based on their graph structure.
        2.
        2023.06 구독 인증기관 무료, 개인회원 유료
        Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.
        4,600원
        3.
        2023.05 KCI 등재 구독 인증기관 무료, 개인회원 유료
        In this study, a bipolar visible light responsive photocatalytic fuel cell (PFC) was constructed by loading a Z-scheme g-C3N4/ carbon black/BiOBr and a Ti3C2/ MoS2 Schottky heterojunction on the carbon brush to prepare the photoanode and photocathode, respectively. It greatly improved the electron transfer and achieved efficient degradation of organic pollutants such as antibiotics and dyes simultaneously in two chambers of the PFC system. The Z-scheme g-C3N4/carbon black/BiOBr formed by adding highly conductive carbon black to g-C3N4/BiOBr not only effectively separates the photogenerated carriers, but also simultaneously retains the high reduction of the conduction band of g-C3N4 and the high oxidation of the valence band of BiOBr, improving the photocatalytic performance. The exceptional performance of Ti3C2/ MoS2 Schottky heterojunction originated from the superior electrical conductivity of Ti3C2 MXene, which facilitated the separation of photogenerated electron–hole pairs. Meanwhile, the synergistic effect of the two photoelectrodes further improved the photocatalytic performance of the PFC system, with degradation rates of 90.9% and 99.9% for 50 mg L− 1 tetracycline hydrochloride (TCH) and 50 mg L− 1 rhodamine-B (RhB), respectively, within 180 min. In addition, it was found that the PFC also exhibited excellent pollutant degradation rates under dark conditions (79.7%, TCH and 97.9%, RhB). This novel pollutant degradation system is expected to provide a new idea for efficient degradation of multiple pollutant simultaneously even in the dark.
        4,900원
        9.
        2023.03 KCI 등재 구독 인증기관 무료, 개인회원 유료
        Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.
        4,500원
        10.
        2023.02 KCI 등재 구독 인증기관 무료, 개인회원 유료
        Carbon nanotube fiber is a promising material in electrical and electronic applications, such as, wires, cables, batteries, and supercapacitors. But the problem of joining carbon nanotube fiber is a main obstacle for its practical development. Since the traditional joining methods are unsuitable because of low efficiency or damage to the fiber structure, new methods are urgently required. In this study, the joining between carbon nanotube fiber was realized by deposited nickel–copper doublelayer metal via a meniscus-confined localized electrochemical deposition process. The microstructures of the double-layer metal joints under different deposition voltages were observed and studied. It turned out that a complete and defect-free joint could be fabricated under a suitable voltage of 5.25 V. The images of the joint cross section and interface between deposited metal and fiber indicated that the fiber structure remained unaffected by the deposited metal, and the introduction of nickel improved interface bonding of double-layer metal joint with fiber than copper joint. The electrical and mechanical properties of the joined fibers under different deposition voltages were studied. The results show that the introduction of nickel significantly improved the electrical and mechanical properties of the joined fiber. Under a suitable deposition voltage, the resistance of the joined fiber was 37.7% of the original fiber, and the bearing capacity of the joined fiber was no less than the original fiber. Under optimized condition, the fracture mode of the joined fibers was plastic fiber fracture.
        4,500원
        11.
        2023.02 KCI 등재 구독 인증기관 무료, 개인회원 유료
        Graphite felt is a felt-like porous material made of high-temperature carbonized polymers. It is widely used in electrode materials because of its good temperature resistance, corrosion resistance, large surface area and excellent electrical conductivity. In this paper, the surface functional group modification is of graphite felt electrodes (mainly nitrogen doping modification, nitrogen–sulfur or nitrogen–boron co-doping modification) and surface catalytic modification (metal/ion surface modification and metal oxide surface modification as Main). There are two main methods and research progresses to improve the performance of graphite felt electrodes, and the comprehensive performance of surface functional group-modified graphite felt electrodes and surface catalytically modified graphite felt electrodes are compared respectively. The results show that both surface functional group modification and surface catalytic modification can improve the comprehensive performance of graphite felt electrodes. In this paper, the future development direction of graphite felt activation modification is also prospected.
        4,900원
        15.
        2022.05 KCI 등재 구독 인증기관 무료, 개인회원 유료
        Coal-based graphite has become the main material of emerging industries. The microstructure of coal-based graphite plays an important role in its applications in many fields. In this paper, the effect of carbon disulfide/N-methyl-2-pyrrolidone solvent mixture extraction on the microstructure of bituminous coal-based graphite was systematically studied through preliminary extraction coupled with high-temperature graphitization. The graphitization degree g (75.65%) of the coal residue-based graphite was significantly higher than that of the raw coal-based graphite. The crystallite size La of the coal residue-based graphite was reduced by 47.06% compared with the raw coal-based graphite. The ID/ IG value of the coal residue-based graphite is smaller than that of the raw coal-based graphite. The specific surface area (16.72 m2/ g) and total pore volume (0.0567 m3/ g) of the coal residue-based graphite are increased in varying degrees compared with the raw coal-based graphite. This study found a carbon source that can be used to prepare coal-based graphite with high graphitization degree. The results are expected to provide a theoretical basis for further clean and efficient utilization of the coal residue resources.
        4,000원
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