Simulating the Formation of Protective Colors: Improvement of Experiments in Teaching

Students are very interested in the Inquiry experiment of Simulating the formation of protective colors. However, it is often influenced by the subjective factors of students in practice, so the simulation experiment is improved twice in teaching. After the second improvement, the experiment achieves good results

Towards Interactive Image Inpainting via Sketch Refinement

One tough problem of image inpainting is to restore complex structures in the corrupted regions. It motivates interactive image inpainting which leverages additional hints, e.g., sketches, to assist the inpainting process. Sketch is simple and intuitive to end users, but meanwhile has free forms with much randomness. Such randomness may confuse the inpainting models, and incur severe artifacts in completed images. To address this problem, we propose a two-stage image inpainting method termed SketchRefiner. In the first stage, we propose using a cross-correlation loss function to robustly calibrate and refine the user-provided sketches in a coarse-to-fine fashion. In the second stage, we learn to extract informative features from the abstracted sketches in the feature space and modulate the inpainting process. We also propose an algorithm to simulate real sketches automatically and build a test protocol with different applications. Experimental results on public datasets demonstrate that SketchRefiner effectively utilizes sketch information and eliminates the artifacts due to the free-form sketches. Our method consistently outperforms the state-of-the-art ones both qualitatively and quantitatively, meanwhile revealing great potential in real-world applications. Our code and dataset are available

Large Language Models for Intent-Driven Session Recommendations

Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to their presumption of a uniform number of intents across all sessions. This assumption overlooks the dynamic nature of user sessions, where the number and type of intentions can significantly vary. In addition, these methods typically operate in latent spaces, thus hinder the model's transparency.Addressing these challenges, we introduce a novel ISR approach, utilizing the advanced reasoning capabilities of large language models (LLMs). First, this approach begins by generating an initial prompt that guides LLMs to predict the next item in a session, based on the varied intents manifested in user sessions. Then, to refine this process, we introduce an innovative prompt optimization mechanism that iteratively self-reflects and adjusts prompts. Furthermore, our prompt selection module, built upon the LLMs' broad adaptability, swiftly selects the most optimized prompts across diverse domains. This new paradigm empowers LLMs to discern diverse user intents at a semantic level, leading to more accurate and interpretable session recommendations. Our extensive experiments on three real-world datasets demonstrate the effectiveness of our method, marking a significant advancement in ISR systems

Antibacterial Activity of Red Pigment Extracted from Pitaya Peel and Its Indication Effect on the Freshness of Bighead Carp Meat

In this study, the red pigment of pitaya peel was extracted by ultrasound-assisted ethanol extraction method. Followed by inhibition zone testing method (using drug sensitive testing paper) and DPPH free radical scavenging method, the antibacterial activity and total antioxidant capacity of the two red pigments were compared. Meanwhile, by detecting the response of the red pigment extracts to the index of both pH change and color difference values, the optimal concentration of the red pigment extract to make an indicator card was selected. Then evaluated the effect of the indicator card on detecting the freshness of bighead carp meat by assessing the changes in pH and TBA values during storage at room temperature. The results showed that the antibacterial activity against E. coli and S. aureus and total antioxidant capacity of red pigment extracted from the red-fleshed pitaya fruit peel was significantly higher than that from the peel of white-fleshed pitaya (P<0.05). The solution of the red pigment extracts of the red-fleshed pitaya also had a sensitive color change reaction to pH2~13. The freshness indicator card was then prepared by using 10 g/L red pigment extracted from the peel of red-fleshed pitaya, and was used to detect the changes of the bighead carp meat. Results showed that during the storage at room temperature (25 ℃) for 12 hours, the pH value of the fish meat first slightly decreased and then increased, while the TBA value continuously increased to 1.96 times that of 0 hours, which indicated a decrease in fish freshness. Meanwhile, the color difference value (ΔE) of the indicator card placed with fish meat also increased from 1.40 (0 h) at the beginning to 18.30 (12 h) at the end, and the color of the peripheral circle of the indicator card changed from light red to rose red to earthy yellow, indicating the deterioration of fish quality more intuitively. This study provides the scientific basis for the comprehensive utilization of the natural red pigment in pitaya fruit peel, also with a preliminary reference for the new food packaging in the aquatic sector of prepackaged food industry

Research Article Performance Monitoring and Analysis of the Photovoltaic Power Generation System Based on the PCI Data Acquisition Card

Abstract: In order to analyze the performance monitoring of the photovoltaic power generation system and achieve the optimal control between the energy storage and consumption, the paper has built a multifunctional performance monitoring system based on the virtual instrument technology. The voltage, current, power, environmental temperature and light intensity are collected via the 1716L-PCI data acquisition card and displayed in real time. After the analysis of the collected data, the system explores the performance of the photovoltaic power generation system. Meanwhile, in order to improve energy use efficiency, the system has set different control modes, including automatic mode, manual mode and custom mode, to discuss the optimal control between the load and the storage energy. The experiment results show that the system has flexible control ability, feasible analysis results and pratical value

Platforms for Parallel Processing of Task on GPU

Import 05/08/2014Tato bakalářská práce se zabývá zpracováním úloh na grafické kartě. Konkrétním typem úloh jsou paralelní třídící algoritmy. V první části práce se vyskytuje popis technologií CUDA a OpenCL, ve kterých je později třídící algoritmus implementován. Dále je rozebrán princip daného algoritmu a jeho implementace. Následuje profilování a optimalizace třídícího algoritmu. V poslední částí je testování algoritmů na různých grafických kartách a porovnání obou technologií.This thesis deals with the processing tasks to the graphics card. Specific types of tasks are selected sorting algorithms. The first part includes description CUDA and OpenCL technology in which sorting algorithm is implemented. Next it is described the principle of the algorithm and its implementation. Next step is profiling and optimization of sorting algorithm. The last part includes testing these algorithms on different graphics cards and a comparison of both technologies.460 - Katedra informatikydobř

A Piecewise Hysteresis Model for a Damper of HIS System

A damper of the hydraulically interconnected suspension (HIS) system, as a quarter HIS, is prototyped and its damping characteristic is tested to characterize the damping property. The force-velocity characteristic of the prototype is analyzed based on a set of testing results and accordingly a piecewise hysteresis model for the damper is proposed. The proposed equivalent parametric model consists of two parts: hysteresis model in low speed region and saturation model in high speed region which are used to describe the hysteresis phenomenon in low speed and nonhysteresis phenomenon in high speed, respectively. The parameters of the model are identified based on genetic algorithm by setting the constraints of parameters according to their physical significances and the corresponding testing results. The advantages of the model are highlighted by comparing to the nonhysteresis model and the permanent hysteresis model. The numerical simulation results are compared with the testing results to validate the accuracy and effectiveness of the proposed model. Finally, to further verify the proposed model’s wide applicability under different excitation conditions, its results are compared to the testing results in three-dimensional space. The research in this paper is significant for the dynamic analysis of the HIS vehicle

Theoretical line loss calculation method for low-voltage distribution network via matrix completion and ReliefF-CNN

Line loss is directly responsible for the management profitability of the grid company. The traditional method of calculating the theoretical line loss for Low Voltage Distribution Networks (LVDN) necessitates more electrical parameters. which cannot be obtained easily. Besides, due to the backward communication conditions of LVDN, the problem of smart meter data missing is significant, which poses a challenge to an exact theoretical line loss calculation. In an attempt to solve the issues above, a theoretical line loss computation approach via matrix completion and ReliefF-convolutional neural network (CNN) for LVDN is proposed. Firstly, a feature weighting algorithm based on ReliefF is presented to analyze the relevance of the electrical parameters, which can be obtained easily. Secondly, a theoretical line loss calculation method is proposed for CNN-based. In the view of the data missing problem, a matrix completion method based on singular value thresholding (SVT) is introduced to obtain the high-precision data, in order to enhance the calculation accuracy of the theoretical line loss calculation. Finally, the proposed method is tested on the data sample of 789 LVDNs. The results show that comparing with CNN, back-propagation and other methods, the mean absolute percentage error (MAPE) of the presented method can reduce by more than 90%. When data missing, the MAPE of the proposed method can reduce by more than 95% compared with the method without considering the data completion

Recycling air-cooled blast furnace slag in fiber reinforced alkali-activated mortar

Fiber reinforced alkali-activated materials (FR-AAM) present as one type of sustainable and resilient materials. However, the thermal degradation mechanism of FR-AAM remains unclear. In this study, FR-AAM incorporating air-cooled blast furnace slag (ACBF), ground granulated blast furnace slag (GGBS) and different types of fibers (steel, glass, and polypropylene) are produced and exposed to elevated temperatures. Test results show that ACBF (replacing 30% of river sand) improved the thermal resistance of FR-AAM due to the ameliorated interfacial transition zone (ITZ) and channels for the release of vapor pressure. Relatively, steel fibers better retain mechanical performance, whilst polypropylene fibers better provide channels for the release of vapor pressure after melting. Gel decomposition and micro crack development are the main causes for the thermal deterioration of FR-AAM. Based on non-destructive tests, damage degree is proposed to quantitatively evaluate the usability and deterioration coefficient (K) is adopted to controll the strength retention of FR-AAM at high temperatures. Economically and environmentally, the development of FR-AAM is promising in shaping a sustainable and resilient future.The authors would like to thank the financial supports from the Science and Technology Research and Development Program Project of China railway group limited (Key Project, No.:2021-Key-08)