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  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Current Issues in Molecular Biology Vol. 45, No. 2 ( 2023-02-04), p. 1314-1332
    In: Current Issues in Molecular Biology, MDPI AG, Vol. 45, No. 2 ( 2023-02-04), p. 1314-1332
    Abstract: The network pharmacology (NP) approach is a valuable novel methodology for understanding the complex pharmacological mechanisms of medicinal herbs. In addition, various in silico analysis techniques combined with the NP can improve the understanding of various issues used in natural product research. This study assessed the therapeutic effects of Arum ternata (AT), Poria cocos (PC), and Zingiber officinale (ZO) on hyperlipidemia after network pharmacologic analysis. A protein–protein interaction (PPI) network of forty-one key targets was analyzed to discover core functional clusters of the herbal compounds. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) term enrichment analysis identified significant categories of hypolipidemic mechanisms. The STITCH database indicated a high connection with several statin drugs, deduced by the similarity in targets. AT, PC, and ZO regulated the genes related to the energy metabolism and lipogenesis in HepG2 cells loaded with free fatty acids (FFAs). Furthermore, the mixture of three herbs had a combinational effect. The herbal combination exerted superior efficacy compared to a single herb, particularly in regulating acetyl-CoA carboxylase (ACC) and carnitine palmitoyltransferase 1 (CPT-1). In conclusion, the network pharmacologic approach was used to assess potential targets of the herbal combination for treatment. Experimental data from FFA-induced HepG2 cells suggested that the combination of AT, PC, and ZO might attenuate hyperlipidemia and its associated hepatic steatosis.
    Type of Medium: Online Resource
    ISSN: 1467-3045
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2090836-2
    SSG: 12
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  • 2
    In: Sensors, MDPI AG, Vol. 20, No. 8 ( 2020-04-24), p. 2430-
    Abstract: Ultra-wideband (UWB) wireless communication is prospering as a powerful partner of the Internet-of-things (IoT). Due to the ongoing development of UWB wireless communications, the demand for high-speed and medium resolution analog-to-digital converters (ADCs) continues to grow. The successive approximation register (SAR) ADCs are the most powerful candidate to meet these demands, attracting both industries and academia. In particular, recent time-interleaved SAR ADCs show that multi-giga sample per second (GS/s) can be achieved by overcoming the challenges of high-speed implementation of existing SAR ADCs. However, there are still critical issues that need to be addressed before the time-interleaved SAR ADCs can be applied in real commercial applications. The most well-known problem is that the time-interleaved SAR ADC architecture requires multiple sub-ADCs, and the mismatches between these sub-ADCs can significantly degrade overall ADC performance. And one of the most difficult mismatches to solve is the sampling timing skew. Recently, research to solve this timing-skew problem has been intensively studied. In this paper, we focus on the cutting-edge timing-skew calibration technique using a window detector. Based on the pros and cons analysis of the existing techniques, we come up with an idea that increases the benefits of the window detector-based timing-skew calibration techniques and minimizes the power and area overheads. Finally, through the continuous development of this idea, we propose a timing-skew calibration technique using a comparator offset-based window detector. To demonstrate the effectiveness of the proposed technique, intensive works were performed, including the design of a 7-bit, 2.5 GS/s 5-channel time-interleaved SAR ADC and various simulations, and the results prove excellent efficacy of signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) of 40.79 dB and 48.97 dB at Nyquist frequency, respectively, while the proposed window detector occupies only 6.5% of the total active area, and consumes 11% of the total power.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 3
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  Actuators Vol. 11, No. 12 ( 2022-11-29), p. 352-
    In: Actuators, MDPI AG, Vol. 11, No. 12 ( 2022-11-29), p. 352-
    Abstract: The dynamic-parameter identification process for developing a suitable precise mathematical model for the implementation and operation of parallel-link robots has received attention. In this study, an efficient and reliable system-identification method for a delta robot is proposed. The parallel-link robot’s dynamic behavior was mathematically modeled according to the principle of virtual work. The dynamic equations of motion are extended to the system of equations that explicitly characterizes the inertial and centripetal/Coriolis forces, and the frictional effects on the robot’s dynamic behavior. Next, the dynamic-parameter identification technique is presented to directly estimate a set of uncertain parameters that are included in the extended dynamic model. In addition, the development of the dynamic model with a generalized inertia matrix for determining the impact of the inertia-coupling characteristic on the robot’s dynamic behaviors is examined. Experimental results indicate that the proposed parameter-estimation technique is an extremely useful tool that can achieve the high-quality identification of an analytic dynamic model for a parallel-link robot.
    Type of Medium: Online Resource
    ISSN: 2076-0825
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2682469-3
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  • 4
    In: Mathematics, MDPI AG, Vol. 8, No. 11 ( 2020-11-12), p. 2016-
    Abstract: In this research, an easy-to-use particle swarm optimizer (PSO) for solving constrained engineering design problems involving mixed-integer-discrete-continuous (MIDC) variables that adopt two kinds of diversity-enhancing mechanisms to achieve superior reliability and validity was developed. As an initial diversity-boosting tool, the local neighborhood topology of each particle is set up such that information exchange is restricted to a limited number of consecutively numbered particles. This topological mechanism forces each particle to move in the search space while interacting only with its neighboring subpopulation. The second diversity-enhancing task is to ensure that the exploration behavior of each particle in the search space is governed such that it follows the diversity classifier decision applied to its subpopulation. This diversity classification iteratively adjusts the three-phase velocity-related mechanism of each particle such that it approaches or retreats from its previous best position/the current best position among the subpopulation. In summary, this PSO tool not only introduces the social interaction of the particle within its cyclically neighboring subpopulation but also exploits the three-phase velocity behavior law governed by the distributed diversity measures categorized for each neighboring subpopulation. This scheme has superior reliability, as well as high practicality for engineering optimization problems involving MIDC variables, which are handled by the widely adopted straightforward rounding-off technique used in most swarm-inspired metaheuristic search technologies.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704244-3
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  Electronics Vol. 11, No. 11 ( 2022-05-25), p. 1685-
    In: Electronics, MDPI AG, Vol. 11, No. 11 ( 2022-05-25), p. 1685-
    Abstract: Deep reinforcement learning (DRL) enables autonomous vehicles to perform complex decision making using neural networks. However, previous DRL networks only output decisions, so there is no way to determine whether the decision is proper. Reinforcement learning agents may continue to produce wrong decisions in unexpected environments not encountered during the learning process. In particular, one wrong decision can lead to an accident in autonomous driving. Therefore, it is necessary to indicate whether the action is a reasonable decision. As one such method, uncertainty can inform whether the agent’s decision is appropriate for practical application where safety must be guaranteed. Therefore, this paper provides uncertainty in the decision by proposing DeepSet-Q with Gaussian mixture (DwGM-Q), which converges the existing DeepSet-Q and mixture density network (MDN). Calculating uncertainty with the Gaussian mixture model (GMM) produced from MDN made it possible to calculate faster than the existing ensemble method. Moreover, it verified how the agent responds to the unlearned situation through the Simulation of Urban MObility (SUMO) simulator and compared the uncertainty of the decision between the learned and nontrained situation.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662127-7
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  • 6
    Online Resource
    Online Resource
    MDPI AG ; 2018
    In:  Energies Vol. 11, No. 4 ( 2018-04-05), p. 855-
    In: Energies, MDPI AG, Vol. 11, No. 4 ( 2018-04-05), p. 855-
    Type of Medium: Online Resource
    ISSN: 1996-1073
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2437446-5
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  • 7
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 16 ( 2023-08-11), p. 3992-
    In: Remote Sensing, MDPI AG, Vol. 15, No. 16 ( 2023-08-11), p. 3992-
    Abstract: Object detection is one of the vital components used for autonomous navigation in dynamic environments. Camera and lidar sensors have been widely used for efficient object detection by mobile robots. However, they suffer from adverse weather conditions in operating environments such as sun, fog, snow, and extreme illumination changes from day to night. The sensor fusion of camera and lidar data helps to enhance the overall performance of an object detection network. However, the diverse distribution of training data makes the efficient learning of the network a challenging task. To address this challenge, we systematically study the existing visual and lidar features based on object detection methods and propose an adaptive feature attention module (AFAM) for robust multisensory data fusion-based object detection in outdoor dynamic environments. Given the camera and lidar features extracted from the intermediate layers of EfficientNet backbones, the AFAM computes the uncertainty among the two modalities and adaptively refines visual and lidar features via attention along the channel and the spatial axis. The AFAM integrated with the EfficientDet performs the adaptive recalibration and fusion of visual lidar features by filtering noise and extracting discriminative features for an object detection network under specific environmental conditions. We evaluate the AFAM on a benchmark dataset exhibiting weather and light variations. The experimental results demonstrate that the AFAM significantly enhances the overall detection accuracy of an object detection network.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 8
    In: Sustainability, MDPI AG, Vol. 11, No. 24 ( 2019-12-14), p. 7172-
    Abstract: This paper examines the effectiveness of South Korea’s policy for reducing greenhouse gas (GHG) emissions in office buildings and evaluates if national targets can be met. A sample of office buildings was categorized into two groups—conventional (Group A) and new (Group B)—based on when their construction was approved. Furthermore, data regarding the three design elements of the building envelope, that is building form, window systems, and U-values were collected. By statistically processing data of each element, reference building models were developed and a case study was conducted for each building. Design changes that were incorporated, keeping in mind the GHG reduction policy, showed 13.1% of saving energy in case 8 (reference building of Group B) than case 1 (reference building of Group A). The savings in case 8 were more than the average GHG reduction rate (12.8%) compared to business as usual (BAU). However, case 4 (a conventional (Group A) building form with new (Group B) window systems and U-values) achieved the greatest savings in building loads. The policy to enhance insulation in new buildings to reduce GHG emissions in the building sector has prompted changes in building forms and window systems and has reduced emissions by 10%, that is 3% more than the expected value. Thus, new innovations in building envelope design could achieve an average 12.8% reduction in emissions in buildings.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2518383-7
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 13 ( 2020-07-02), p. 4598-
    In: Applied Sciences, MDPI AG, Vol. 10, No. 13 ( 2020-07-02), p. 4598-
    Abstract: The automated optical inspection of a surface mount technology line inspects a printed circuit board for quality assurance, and subsequently classifies the chip assembly defects. However, it is difficult to improve the accuracy of previous defect classification methods using full chip component images with single-stream convolutional neural networks due to interference elements such as silk lines included in a printed circuit board image. This paper proposes a late-merge dual-stream convolutional neural network to increase the classification accuracy. Two solder regions are extracted from a printed circuit board image and are input to a convolutional neural network with a merge stage. A new convolutional neural network structure is then proposed that is able to classify for defects. Since defect features are concentrated in solder regions, the classification accuracy is increased. In addition, the network weight is reduced due to a reduction of the input data. Experimental results for the proposed method show a 5.3% higher performance in F1-score than a single-stream convolutional neural network based on full chip component images.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 10
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Actuators Vol. 9, No. 2 ( 2020-06-15), p. 45-
    In: Actuators, MDPI AG, Vol. 9, No. 2 ( 2020-06-15), p. 45-
    Abstract: The output feedback signal of the electro-hydraulic valve system (EHVS) affects the activation of its right or left envelope function; thus, even weak measurement noise can cause high-frequency switching between the two envelope functions, leading to chattering in the control input. Consequently, feedforward and feedback controllers in a cascaded configuration generate undesirable chattering in the output signal. We propose a practical and reliable control approach for an EHVS actuated by a proportional control valve. The proposed controller has a parallel structure comprising an inverse generalized Prandtl–Ishlinskii (P–I) model-based feedforward controller, with both hydraulic dead-zone and flow saturation limits, for compensating asymmetric hysteretic behavior. Further, the proposed controller comprises a robust proportional-integral-derivative (PID) feedback controller for achieving robustness against disturbances and noises. The proposed parallel structure is independent of the output feedback of the EHVS. Moreover, the proposed robust PID feedback controller guarantees EHVS stability by precisely selecting the cutoff frequency for the sensitivity and complementary sensitivity functions based on the amplitude spectrum of the inverse-model-based feedforward compensation error. The results verify the high reliability of the proposed EHVS control scheme for the precise control of an EHVS actuated by a proportional control valve in practice.
    Type of Medium: Online Resource
    ISSN: 2076-0825
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2682469-3
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