Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation

Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/Comment: This paper has been accepted by ACM Multimedia 202

Improving Zero-Shot Generalization for CLIP with Synthesized Prompts

With the growing interest in pretrained vision-language models like CLIP, recent research has focused on adapting these models to downstream tasks. Despite achieving promising results, most existing methods require labeled data for all classes, which may not hold in real-world applications due to the long tail and Zipf's law. For example, some classes may lack labeled data entirely, such as emerging concepts. To address this problem, we propose a plug-and-play generative approach called \textbf{S}ynt\textbf{H}es\textbf{I}zed \textbf{P}rompts~(\textbf{SHIP}) to improve existing fine-tuning methods. Specifically, we follow variational autoencoders to introduce a generator that reconstructs the visual features by inputting the synthesized prompts and the corresponding class names to the textual encoder of CLIP. In this manner, we easily obtain the synthesized features for the remaining label-only classes. Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features. Extensive experiments on base-to-new generalization, cross-dataset transfer learning, and generalized zero-shot learning demonstrate the superiority of our approach. The code is available at \url{https://github.com/mrflogs/SHIP}.Comment: Accepted by ICCV 202

Research Hotspot and Trend Analysis of China’s Elderlyoriented Smart Products

The Chinese government attaches great importance to the current situation of population aging, so it has introduced relevant aging policies. The combination of new technologies has played a positive role in the development of Elderly-oriented smart products of enterprises. Based on the research literature on Elderly-oriented smart products collected in CNKI database in recent ten years (2012-2022), this paper makes a quantitative analysis on the research results of Elderly-oriented smart products in China with the help of CiteSpace visual analysis software. Through research hotspots and evolution trends, it is found that the theme can be extended: the upgrading and construction of Elderly-oriented smart products will be a hot research topic in the academic community in the future

Towards Optimizing Garlic Combine Harvester Design with Logistic Regression

In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel and a lifting chain. Each part had unique structural parameters and motion parameters, as different parameters would deeply affect the performance of the machine. A logistical regression algorithm was utilized to analyze the working speed of the reel, the digging depth of the reciprocating cutter and the lifting speed of the lifting chain. This paper also discussed the influence of these three functions on the damage rate based on the collected data when harvesting garlic. Specifically, each function was tested 60 times for collecting data. The experimental results showed that the order of influence of the three functions on the damage rate was the digging depth, working speed and lifting speed. Moreover, the lowest damage rate was 0.18% when the digging depth was 100 mm, the working speed was 1.05 km·h−1 and the lifting speed was 0.69 m·s−1. A validation test was taken out based on the three functions of the analysis results, and the damage rate was 0.83%, which was close to the analysis results, and proved that the analysis results were accurate and meaningful. The research results are beneficial to the development and application of the garlic combine harvester

Potential use of LIAD time-of-flight mass spectrometry for the detection of biomolecules: An example of detecting nucleobases in DNA

Deoxyribonucleic acid (DNA) carries the genetic information necessary for the synthesis of RNA and proteins; it is a biological macromolecule essential for the development and proper functioning of living organisms and is composed of nucleobases, deoxyribose, and phosphate. The four nucleobases in DNA are adenine (AD), guanine (GU), thymine (TY), and cytosine (CY). Abnormal concentrations of these four nucleobases in an organism have a significant impact on disease diagnosis. Therefore, the qualitative and quantitative detection of these DNA nucleobases in organisms is helpful to diagnose certain diseases. In this work, we report the simultaneous determination of purine (AD, GU) and pyrimidine (TY, CY) nucleobases in DNA using laser-induced acoustic desorption (LIAD) with electron ionization (EI)/time-of-flight mass spectrometry (TOFMS). The purine (MW 120 Da) samples were used as model compounds to assess the sensitivity and quantitative performance of the instrument. Its limits of detection assessed using the LIAD/EI/MS method were ∼0.5–1.2 pg under optimal conditions, and their calibration curves exhibited good linearity (R2 = 0.98). The LIAD/TOFMS was successfully applied in the simultaneous detection of AD, GU, TY, and CY in real DNA samples. The advantage of this technique is simple, fast, and without complex pre-treatment processes. In addition, a quartz-enhanced LIAD (QE-LIAD) source was used to improve the signal strength. The desorption for complex biomolecules shows that the QE-LIAD is still a “gentle” desorption source

Research Hotspot and Trend Analysis of China’s Elderlyoriented Smart Products

The Chinese government attaches great importance to the current situation of population aging, so it has introduced relevant aging policies. The combination of new technologies has played a positive role in the development of Elderly-oriented smart products of enterprises. Based on the research literature on Elderly-oriented smart products collected in CNKI database in recent ten years (2012-2022), this paper makes a quantitative analysis on the research results of Elderly-oriented smart products in China with the help of CiteSpace visual analysis software. Through research hotspots and evolution trends, it is found that the theme can be extended: the upgrading and construction of Elderly-oriented smart products will be a hot research topic in the academic community in the future

Research of Flow Characteristics Hybrid Model of Steam Turbine Stage Based on the Improved PSO Algorithm

Power station steam turbine stage flow characteristics show the Corresponding relationship between pressure and flow rate, which is the important research foundation for analysis of steam turbine performance and the further optimization analysis of unit. Based on strict theory analysis, this article obtained two important key characteristic coefficients such as the capacity of flow coefficient and the level of group critical pressure ratio which mainly influenced the turbine characteristics. And then the secondary flow calculation model was imposed combining with the massive actual data, adapting the method of improved PSO algorithm. The practical results show that, the obtained model not only ensured good regularity and ductility, but also has higher calculation precision. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.350

The Research of Power plant Operating Data Based on Real-time Digital filtration technology

Real-time monitoring of the data of the thermal power plant is the basis of accurate analyzing thermal economy and accurate reconstruction of the operating state. Due to noise is inevitable, we need real-time monitoring data filtering to get accurate information of units and equipment in the operating data of thermal power plant. Real-time filtering algorithm can’t be used to correct the current data with future data. Compared with traditional filtering algorithm, there are a lot of constraints. First-order lag filtering method and weighted recursive average filtering method can be used for real-time filtering. This paper analyzes the characteristics of the two filtering methods and applications for real-time processing of the positive spin simulation data, and the thermal power plant operating data. The analysis revealed that the weighted recursive average filtering method applied to the simulation and real-time plant data filtering achieved very good results. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.346

Galvanic Replacement Preparation of Spindle-Structured Sb@C@NC as Anode for Superior Lithium-Ion Storage

Antimony (Sb) is regarded to be a potential alloying-type anode for lithium-ion batteries due to its excellent electrochemical reversibility and high theoretical specific capacity (660 mA h g−1). However, huge volume expansion accompanying rapid capacity fading seriously hinders its commercial application. Herein, double-carbon-modified spindle-structured Sb@C@NC were constructed via galvanic replacement using a Fe-based metal-organic framework (MOF) with polydopamine-coated-derived Fe@C@NC as reactants. Due to the unique double-carbon-encapsulated structure, the Sb@C@NC anode effectively moderates the volume fluctuation and maintains the integral framework from collapsing during the annealing and cycling process. As lithium-ion battery (LIB) anodes, Sb@C@NC attained excellent cycling performance (389 mAh g−1 at 100 mA g−1 after 100 cycles) and superior rate capability (a reversible capacity of 343 mAh g−1 at 2000 mA g−1). Such an MOF-based approach provides an adjustable strategy for Sb-based nanomaterial and shield light on the applications of Sb@C@NC in other fields