High Resource Utilization Efficiency Research Articles

Fractal theory based identification model for surface crack of building structures

The existing building structures are mainly composed of concrete and masonry structures. This paper proposes a Fractal Theory-based Method (FTM) designed to efficiently detect surface cracks in both concrete and masonry building structures, addressing limitations in existing crack recognition techniques, such as weak adaptability to complex building surfaces and requiring a large amount of computational and human resource. A novel Soft Box-Counting (SBC) algorithm is proposed to calculate texture roughness information from multi-channel and multi-scale crack images, effectively mitigating the influence of non-feature information on crack identification results. The SBC algorithm is integrated into neural networks through the fractal embedding mechanism, enhancing feature analysis through visualization experiments. The resulting FTM model integrates texture roughness information from crack images and employs fractal theory to accurately classify crack regions while minimizing the similarity between crack and non-crack regions. In comparison to existing models, FTM demonstrates superior identification performance at the same computational level on benchmark datasets for surface cracks in both concrete and masonry building structures. The FTM model exhibits enhanced identification universality and higher resource utilization efficiency.

Dynamic Event-Triggered Strategy-Based Optimal Control of Modular Robot Manipulator: A Multiplayer Nonzero-Sum Game Perspective.

Due to the limited computing and processing ability of modular robot manipulator (MRM) components, such as sensors and controllers, event-triggered mechanisms are considered a crucial communication paradigm shift in resource constrained applications. Dynamic event-triggered mechanism is developing into a new technology by reason of its higher resource utilization efficiency and more flexible system design requirements than traditional event-triggered. Therefore, an optimal control scheme of multiplayer nonzero-sum game based on dynamic event-triggered is developed for MRM systems with uncertain disturbances. First, dynamic model of the MRM is established according to joint torque feedback technique and model uncertainty is estimated by data-driven-based neural network identifier. In the framework of differential game, the tracking control problem of MRM system is transformed into the optimal control problem for multiplayer nonzero-sum game with the control input of each joint module as the player. Then, the static event-triggered control problem of MRM system is studied based on adaptive dynamic programming algorithm. On this basis, the internal dynamic variable describing the previous state of the system is introduced, and the characteristics of dynamic trigger rule and its relationship with static rule are revealed theoretically. By designing an exponential attenuation signal, the minimum sampling interval of the system is always positive, so that Zeno behavior is excluded. Lyapunov theory proves that the system is asymptotically stable and the experimental results verify the validity of the proposed method.

Analysis of the Investment Value of The Listed Companies in The Liquor Industry Based on The Factor Analysis Method

In the post-epidemic period, the liquor industry has entered a new round of adjustment cycle. This paper takes the average financial data of the annual reports of 18 listed companies in the liquor industry from 2021-2021-2022 as sample data. Through S PSS23.0 data analysis software, the factor analysis method selects the corresponding public factor, and the investment value of listed companies in liquor industry is analyzed according to the comprehensive evaluation of each public factor and weight. According to the scores of various factors, the profitability of Kweichow Moutai is far higher than that of other companies, the development capacity of Laobaigan liquor industry is at the forefront of the industry, and the operating capacity of Swellfun is strong, with high resource utilization efficiency, and Yanghe Stock's solvency and capital utilization efficiency are strong. According to the comprehensive score research, it is found that among the listed companies in the liquor industry with strong profitability, their business ability and solvency are generally weak. At the same time, according to the comprehensive score, the top three enterprises are Shanxi Fenjiu, Luzhou Laojiao and Shide Wine Industry.

Coupling high natural resources and carbon emission efficiency on economic growth in China

China, the world's most populous nation, has seen rapid expansion in recent decades. The burning of fossil fuels has contributed significantly to this increase because of the country's heavy reliance on extracting plentiful natural resources. However, the possible effects on long-term economic development and the environment have caused significant concern. This study focuses on how efficient use of natural resources and reduction in carbon emissions are linked to economic development in China. Using the 2005–2020 entropy weight approach and the coupling coordination degree evaluation method, we investigated the relationship between high natural resource regions, economic stability, infrastructure, and resource utilization efficiency. Our research indicates that combining low carbon emissions and the efficient use of natural resources may help China's economy thrive sustainably. We provide several suggestions for future studies to help Chinese officials strike a better balance between economic development and environmental conservation.

Approaching the mapping limit with closed-loop mapping strategy for deploying neural networks on neuromorphic hardware.

The decentralized manycore architecture is broadly adopted by neuromorphic chips for its high computing parallelism and memory locality. However, the fragmented memories and decentralized execution make it hard to deploy neural network models onto neuromorphic hardware with high resource utilization and processing efficiency. There are usually two stages during the model deployment: one is the logical mapping that partitions parameters and computations into small slices and allocate each slice into a single core with limited resources; the other is the physical mapping that places each logical core to a physical location in the chip. In this work, we propose the mapping limit concept for the first time that points out the resource saving upper limit in logical and physical mapping. Furthermore, we propose a closed-loop mapping strategy with an asynchronous 4D model partition for logical mapping and a Hamilton loop algorithm (HLA) for physical mapping. We implement the mapping methods on our state-of-the-art neuromorphic chip, TianjicX. Extensive experiments demonstrate the superior performance of our mapping methods, which can not only outperform existing methods but also approach the mapping limit. We believe the mapping limit concept and the closed-loop mapping strategy can help build a general and efficient mapping framework for neuromorphic hardware.

Practical Performance Analysis of Interference in DSS System

The 5G network is developing rapidly. However, due to spectrum resource limitation, it is expected to use the 5G network to ensure high resource utilization and network efficiency, while keeping part of 4G in the same band for existing 4G users. Dynamic spectrum-sharing (DSS) technology enables 4G/5G wireless networks to coexist in scarce spectrum resources and dynamically allocates spectrum resources in the same band. 4G/5G DSS has been successfully commercialized in some countries such as Germany and Brazil. However, complex 4G/5G DSS networks will introduce intra-frequency interference in the inter-system, which will affect network performance. Therefore, we innovatively proposed two interference mitigation schemes: buffer setting and rate matching. Furthermore, we have verified the practical performance of both schemes in a commercial network for the first time to determine the feasibility of the schemes. From theory, simulation, and practical analysis, both schemes can effectively mitigate the interference of the inter-system introduced by DSS: increasing the network rate by 60% in the interference environment and improving the user experience in the DSS architecture.

Double-Lead Content Search And Producer Location Prediction Scheme For Producer Mobility In Named Data Networking

Abstract In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer’s movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.

Efficient design and implementation of a distributed temperature sensor for field apparatus

The signal acquisition and processing ability of distributed temperature sensor based on Raman scattering (DTS-Raman) directly determines the accuracy, spatial resolution, temperature resolution, and other key indicators of the entire system. Real-time and effective processing is the key to ensuring the practicability of the field system. Owing to the long distance of the DTS system, the energy of the weak Raman scattering is further reduced when it reaches the avalanche photodiode detector because of fiber loss, which results in a decrease in the signal-to-noise ratio (SNR) and an increase in the temperature measurement error. To improve the SNR and ensure the accuracy of the demodulation temperature, the signal acquisition and processing module must be able to perform real-time and fast processing of large amounts of data. The existing processing structure based on the acquisition board requires a special personal computer and host processing software, which limits the application of DTS system. Therefore, a real-time acquisition and processing scheme based on field-programmable gate array is proposed in this paper. A set of cyclic shift register sequence (CSRS) with Ping-Pang functions is designed to improve the storage efficiency. According to the period of the laser source, the accumulated results stored in the CSRS and echo pulse data are accumulated and shifted in real time to realize circular utilization of storage resources. A DTS-Raman system prototype and test platform are built to verify the effects of the proposed scheme. The experimental results show that the scheme can realize a temperature accuracy within in real time and has a high resource utilization efficiency.

Two-Level Soft RAN Slicing for Customized Services in 5G-and-Beyond Wireless Communications

In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.

Data mining and decision support systems for efficient dairy production.

Gainful livestock farming requires selective breeding of animals with certain heritable desirable traits which gives profitability in terms of farm produce. Modern dairy animals are selected for traits which directly or indirectly contribute to high milk production. The concept of “feed conversion efficiency” in terms of milk production is now vigorously taken up by researchers and farm managers for recognizing and breeding efficient milk-producing animals. The whole concept of economic farming thus requires identification of “elite” animals, meeting above criteria as base population for the farm enterprise. Farmers and animal traders have been selecting best animals based on certain physical characters, which were also accepted by the breeding scientists as phenotypes. Data mining allows uncovering of hidden patterns in the data for better understanding of data relationship for developing suitable models for further improvements. Along with artificial intelligence techniques, data mining has opened new avenues for achieving high resource utilization efficiency and sustainable profitability in livestock production systems. The present review discusses and summarizes various data mining techniques and decision support systems for scientific dairy farming.

Plant community C:N:P stoichiometry is mediated by soil nutrients and plant functional groups during grassland desertification

Grassland has sustained serious desertification due to inappropriate human activity in arid and semi-arid areas. This desertification has disrupted ecosystem structure and function. However, desertification on the grassland's C:N:P stoichiometry (Carbon, Nitrogen, and Phosphorus) are poorly understood, which undermines management and restoration of desertified grassland. To improve our knowledge, we studied a desertification gradient in Northeastern China's Horqin Sandy Land to determine the effects of desertification on ecosystem function, the plant community, and soil C:N:P stoichiometry. The soil and plant community C, N and P pools decreased during desertification. Under severe desertification, the soil C:N decreased by 17.4%, versus 39.2% for C:P and 50.1% for N:P. This implies non-proportional losses of soil organic C and nutrients, with the greatest loss for organic C, followed by total N and total P. Plant aboveground C:N and C:Pand belowground C:N increased, suggesting increased nutrient utilization efficiency. Canonical correspondence analysis ordination showed that soil N and P significantly affected the plant species composition, which was associated with plant C:N:P. The desertified grassland's fertility (especially N) directly determined the plant community C, N, and P concentrations and C:N:P ratios. The increased dominance of two psammophytes, Agriophyllum squarrosum and Artemisia halodendron decreased plant community C, N, and P concentrations, but increased C:N and C:P. Thus, plant community C:N:P stoichiometry was mediated by soil nutrients and plant functional groups driven by soil nutrient changes during desertification. The soil nutrient imbalance potentially changed plant community composition, this then altered the soil chemical composition because plant species differ in their element contents and litter inputs. Hence, species with high N content could improve grassland restoration by mitigating N limitations. The native psammophytes A. squarrosum and A. halodendron, with high stress resistance and resource utilization efficiency, could be applied in desertified grassland restoration.

The Design of a Class of Nonlinear Networked System

A class of nonlinear networked systems with external interference is designed in this paper. Currently, we have witnessed that networked control technology has played a key role in the Internet of Things (IoT). However, the amount of big data in the Internet of Things will cause network congestion in the data transmission of the network control system. In order to solve this problem, event‐driven control scheme can effectively save the network resources of the network control system. But when there is interference in the system, the conventional constant threshold parameter is difficult to achieve the expected energy‐saving effect. In order to solve this challenge, this paper proposes a design with a continuously variable threshold. After each trigger to transmit data, the threshold gets changed accordingly, and the sliding mode approach rate is changed simultaneously. Compared with the constant threshold event drive, the number of transmissions in this design can be greatly reduced, while sliding mode jitter is suppressed. The simulation results show that the scheme can achieve higher resource utilization efficiency and better robustness.

Utilizing comprehensive decision analysis methods to determine an optimal planting pattern and nitrogen application for winter oilseed rape

Oilseed rape is one of the most important oil crops globally. Attaining the appropriate cultivation method (planting pattern and nitrogen level) is necessary to achieve high yield, quality and resource utilization efficiency. However, the optimal method for oilseed rape varies across countries and regions. The objective of the present study was to determine an appropriate cultivation method, including planting pattern and nitrogen application, for winter oilseed rape in northwestern China. Two planting patterns: ridge film mulching and furrow planting (RFMF) and flat planting (FP), and six nitrogen (N) amounts: 0 (N0), 60 (N60), 120 (N120), 180 (N180), 240 (N240), and 300 (N300) kg N ha−1 were applied across three growing seasons (2014–2017). Three comprehensive decision analysis methods: principal component analysis, grey correlation degree analysis and the combined entropy weight and dynamic technique for order preference by similarity to ideal solution method were used to evaluate the growth and physiological indicators, nutrient uptake, yield, quality, evapotranspiration, and water use efficiency of winter oilseed rape. Planting pattern, nitrogen amount and their interaction significantly affected the indicators aforementioned. The RFMF pattern significantly increased all indicators over the FP pattern. Application of N also markedly increased all the indicators except for seed oil content, but the yield, oil production and water use efficiency were decreased when N fertilizer exceeded 180 kg N ha−1 under FP and 240 kg N ha−1 under RFFM. The evaluation results of the three comprehensive decision analysis methods indicated that RFMF planting pattern with 240 kg N ha−1 is an appropriate cultivation method for winter oilseed rape in northwestern China. These findings are of vital significance to maximize yield, optimize quality and improve resource use efficiencies of winter oilseed rape.

Optimal Virtual Network Function Deployment for 5G Network Slicing in a Hybrid Cloud Infrastructure

Network virtualization is a key enabler for 5G systems to support the expected use cases of vertical markets. In this context, we study the joint optimal deployment of Virtual Network Functions (VNFs) and allocation of computational resources in a hybrid cloud infrastructure by taking the requirements of the 5G services and the characteristics of the cloud architecture into consideration. The resulting mixed-integer problem is reformulated as an integer linear problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid infrastructure over a standard cloud radio access network consisting only of a central cloud, and show that the proposed mechanism to deploy VNF chains leads to high resource utilization efficiency and large gains in terms of the number of supported VNF chains. To deal with the computational complexity of optimizing a large number of clouds and VNF chains, we propose a simple low-complexity heuristic that attempts to find a feasible VNF deployment solution with a limited number of functional splits. Numerical results indicate that the performance of the proposed heuristic is close to the optimal one when the edge clouds are well dimensioned with respect to the computational requirements of the 5G services.

R&D Subsidy and Corporate Innovation: An Integrated View of Resource Allocation and Utilization

Prior research on corporate innovation has highlighted the roles of public support especially R&D subsidy. However, there are mixed findings regarding the effect of R&D subsidy on corporate innovation output. In this study, we develop an integrated view of resource allocation and resource utilization to study how this effect may vary according to resource utilization efficiency of firms when firms are awarded R&D subsidy based on different rules. We hypothesize that when R&D subsidy is allocated to a firm based on its technology capability, such subsidy will have stronger positive effect on innovation output with the presence of high resource utilization efficiency. In contrast, when a firm is allocated to R&D subsidy based on political connection, the positive effect of R&D subsidy on innovation output becomes weaker with decreasing resource utilization efficiency. Our results based on data of Chinese privately listed firms between 2010 and 2016 confirm these hypotheses. The findings offer fresh and comprehensive insights on how government allocation rules might influence the ways in which firms use resources to innovate and further influence the effectiveness of public support in emerging countries.

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.

Resource Utilization Efficiencies in a Closed System with Artificial Lighting during Continuous Lettuce Production

New plant production systems with a low environmental impact (or a high resource utilization efficiency) are necessary for urban agriculture development. This study explores the benefits of closed plant production systems (CPPSs) with artificial lighting using a commercial CPPS at Osaka Prefecture University. Lettuce plants were produced continuously as a model for analyzing resource consumption rates, such as electricity, labor, water, and CO2, over two years. Monthly consumption rates of electricity, labor, water, and CO2 increased with the increase in the monthly production rate of the lettuce. The utilization efficiencies (=output/input) of electricity, energy, water, and CO2 were 1.0%, 1.0%, 4.0%, and 32.6%, respectively. If the commercial CPPS maintains the monthly production rate at a higher level, the energy utilization efficiency will increase. The number of air exchanges in a commercial CPPS should decrease to increase water and CO2 utilization efficiencies. Reusing water drained from the air conditioning system and employing a closed loop in the nutrient supply system also contribute to increasing the water utilization efficiency and lowering the environmental impact. Although a commercial CPPS still requires further improvements, it may be a good crop production system for urban agriculture provided resource utilization efficiencies improve.

Mobility Management for Intro/Inter Domain Handover in Software-Defined Networks

To provide satisfactory Quality of Service (QoS) on the move, efficient mobility management is indispensable to provide mobile users with seamless and ubiquitous wireless connectivity. However, both the conventional centralized mobility architecture and the upcoming distributed mobility management face fundamental challenges such as sub-optimal routing, scalability, and so on. The emerging software-defined networking (SDN) architecture can efficiently manage network operations, and accordingly provides a new direction to address the challenges in mobility management. In this paper, we propose an SDN-based Mobility Management (SDN-MM) scheme to support seamless Intro/Inter domain handover with route optimization. SDN-MM decouples mobility management and packet forwarding functions by installing route optimizing and mobility control logics in an SDN controller, but exempting it from traffic redirecting. In SDN-MM, a comprehensive set of signaling operations are designed in order to provide transparent and efficient mobility support for ongoing sessions in each handover scenario, which prevents packet loss and tunneling overhead, and accordingly provide improved QoS to mobile users. For data communications, an SDN controller in SDN-MM pre-calculates the optimal end-to-end route before a handover, and decides whether to migrate traffic to the route by balancing the performance gain and the signaling overhead, which greatly improves bandwidth resource utilization. Finally, we develop a novel analytical model to evaluate the performance of SDN-MM, including signaling overhead, handover latency, and packet delivery cost. The simulation results have been provided to demonstrate that the proposed SDN-MM can greatly improve handover performance and maintain high resource utilization efficiency as well.

El abandono del pastoreo afecta negativamente a la calidad del pasto en pastizales atlánticos ibéricos

La calidad del pasto en los pastizales de montaña es un factor importante para mantener o mejorar el rendimiento animal. Estudios previos han identificado atributos vegetales beneficiosos para la nutrición animal; sin embargo, poco se sabe sobre su mantenimiento en comunidades vegetales. Nuestra principal hipótesis sostiene que el pastoreo favorece a las especies con una alta capacidad de rebrote y una baja eficiencia de utilización de recursos, promoviendo así la producción de pasto de alta calidad. Para testar la hipótesis, realizamos un experimento de exclusión del pastoreo en un pastizal de la montaña atlántica, combinado con un marco teórico de respuesta-efecto basado en varios atributos vegetales y una metodología estadística apropiada para medir la contribución relativa del recambio de especies y la variabilidad intraespecífica de los atributos (ITV) tras el abandono del pastoreo. Nuestros resultados mostraron que la calidad del pasto disminuyó tras el abandono del pastoreo a corto plazo, principalmente debido al efecto de la ITV. Los efectos por recambio de especies podrían aparecer tras un período más largo de abandono si, tal y como es esperable, las gramíneas altas con tejidos de baja calidad nutritiva sustituyen a las especies de mayor calidad.

A Resource-Efficient Multiplierless Systolic Array Architecture for Convolutions in Deep Networks

This brief presents a resource-efficient VLSI architecture for convolution operations in deep networks. Taking advantage of a feature of the max pooling layer in classical convolutional neural networks (CNNs), the image pixels are scaled such that the weight values are constrained to lie in [-1, +1] range. Under this constraint, the weight parameters are chosen as trigonometric functions, enabling realization of convolution without multipliers. In particular, the convolution is realized using the CORDIC algorithm. We also propose a dataflow model based on a reconfigurable systolic ring array to achieve performance comparable to contemporary CNN architectures but with substantially less hardware, high resource utilization efficiency, and reduced power consumption. FPGA implementation of the proposed architecture on Xilinx Virtex-5 XC5VLX5OT achieves roughly 55% higher resource efficiency, with approximately 53% reduction in the slice-delay product and 55% reduction in power consumption compared to recent architectures.