An expert PI controller with dead time compensation of monitor AGC in hot strip mill

Hot strip rolling production is a high-speed process which requires high-speed control and communication system, but because of the long distance between the delivery stand of the finishing mill and the gauge meter, dead time occurs when strip is transported from the site of the actuator to another location where the gauge meter takes its reading, which seriously affects the thickness control effect. According to the process model which is developed based on the measured data, a filtered Smith predictor is applied to predict the thickness deviation of the finishing mill. At the same time, an expert PI controller based on feature information is proposed for the strip thinning during looper rising and coiler biting period and the strip thickening during the tension loss period of the strip tail end. As a result, the thickness accuracy has been improved by about 1.06% at a steady rolling speed and about 1.23% in acceleration and deceleration

Pursuing Equilibrium of Medical Resources via Data Empowerment in Parallel Healthcare System

The imbalance between the supply and demand of healthcare resources is a global challenge, which is particularly severe in developing countries. Governments and academic communities have made various efforts to increase healthcare supply and improve resource allocation. However, these efforts often remain passive and inflexible. Alongside these issues, the emergence of the parallel healthcare system has the potential to solve these problems by unlocking the data value. The parallel healthcare system comprises Medicine-Oriented Operating Systems (MOOS), Medicine-Oriented Scenario Engineering (MOSE), and Medicine-Oriented Large Models (MOLMs), which could collect, circulate, and empower data. In this paper, we propose that achieving equilibrium in medical resource allocation is possible through parallel healthcare systems via data empowerment. The supply-demand relationship can be balanced in parallel healthcare systems by (1) increasing the supply provided by digital and robotic doctors in MOOS, (2) identifying individual and potential demands by proactive diagnosis and treatment in MOSE, and (3) improving supply-demand matching using large models in MOLMs. To illustrate the effectiveness of this approach, we present a case study optimizing resource allocation from the perspective of facility accessibility. Results demonstrate that the parallel healthcare system could result in up to 300% improvement in accessibility

IR Design for Application-Specific Natural Language: A Case Study on Traffic Data

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data

Evolutionary City: Towards a Flexible, Agile and Symbiotic System

Urban growth sometimes leads to rigid infrastructure that struggles to adapt to changing demand. This paper introduces a novel approach, aiming to enable cities to evolve and respond more effectively to such dynamic demand. It identifies the limitations arising from the complexity and inflexibility of existing urban systems. A framework is presented for enhancing the city's adaptability perception through advanced sensing technologies, conducting parallel simulation via graph-based techniques, and facilitating autonomous decision-making across domains through decentralized and autonomous organization and operation. Notably, a symbiotic mechanism is employed to implement these technologies practically, thereby making urban management more agile and responsive. In the case study, we explore how this approach can optimize traffic flow by adjusting lane allocations. This case not only enhances traffic efficiency but also reduces emissions. The proposed evolutionary city offers a new perspective on sustainable urban development, highliting the importance of integrated intelligence within urban systems.Comment: 11 pages, 11 figure

Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITSComment: 6 pages, 6 figures. To be published in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC

Massively parallel pyrosequencing-based transcriptome analyses of small brown planthopper (Laodelphax striatellus), a vector insect transmitting rice stripe virus (RSV)

<p>Abstract</p> <p>Background</p> <p>The small brown planthopper (<it>Laodelphax striatellus</it>) is an important agricultural pest that not only damages rice plants by sap-sucking, but also acts as a vector that transmits rice stripe virus (RSV), which can cause even more serious yield loss. Despite being a model organism for studying entomology, population biology, plant protection, molecular interactions among plants, viruses and insects, only a few genomic sequences are available for this species. To investigate its transcriptome and determine the differences between viruliferous and naïve <it>L. striatellus</it>, we employed 454-FLX high-throughput pyrosequencing to generate EST databases of this insect.</p> <p>Results</p> <p>We obtained 201,281 and 218,681 high-quality reads from viruliferous and naïve <it>L. striatellus</it>, respectively, with an average read length as 230 bp. These reads were assembled into contigs and two EST databases were generated. When all reads were combined, 16,885 contigs and 24,607 singletons (a total of 41,492 unigenes) were obtained, which represents a transcriptome of the insect. BlastX search against the NCBI-NR database revealed that only 6,873 (16.6%) of these unigenes have significant matches. Comparison of the distribution of GO classification among viruliferous, naïve, and combined EST databases indicated that these libraries are broadly representative of the <it>L. striatellus </it>transcriptomes. Functionally diverse transcripts from RSV, endosymbiotic bacteria <it>Wolbachia </it>and yeast-like symbiotes were identified, which reflects the possible lifestyles of these microbial symbionts that live in the cells of the host insect. Comparative genomic analysis revealed that <it>L. striatellus </it>encodes similar innate immunity regulatory systems as other insects, such as RNA interference, JAK/STAT and partial Imd cascades, which might be involved in defense against viral infection. In addition, we determined the differences in gene expression between vector and naïve samples, which generated a list of candidate genes that are potentially involved in the symbiosis of <it>L. striatellus </it>and RSV.</p> <p>Conclusions</p> <p>To our knowledge, the present study is the first description of a genomic project for <it>L. striatellus</it>. The identification of transcripts from RSV, <it>Wolbachia</it>, yeast-like symbiotes and genes abundantly expressed in viruliferous insect, provided a starting-point for investigating the molecular basis of symbiosis among these organisms.</p