Resource Occupation Research Articles

A study on secure coding of intelligent inspection video in plant areas based on improved deep reinforcement learning

To realize effective data compression, reduce resource occupation, and provide a smoother video transmission experience. The security coding method of an intelligent factory detection video based on improved deep reinforcement learning is studied. First, the transmission and network model of the factory inspection video are analyzed, and a conditional depth network based on Transformer is designed. The network learns the image features through a feature extractor, and processes these features through a motion estimation and compression module. Multi-layer and multi-hypothesis motion compensation and a self-encoder are used to encode features based on the predicted features. The optimal coding strategy is learned using the depth deterministic strategy gradient method, and the residual is outputted in the decoding stage to reconstruct the video frame. The experimental results show that this method can realize the security coding of intelligent inspection video in plant area, and the BDBR (Bjontegaard delta bitrate) index can be saved by 68.01%, and the BD-PSNR (Bjontegaard delta PNSR) index can be increased by 3.49 dB on average; 50 iterations minimize the inefficient strategy ratio; The maximum average return is about 152%; It has small memory occupation, low bandwidth requirements, and high real-time video transmission; High user experience rating.

Industrial technological advance and the employment demand for China's labour force-Micro evidence from labour hiring in companies.

Technological advance in industry has complexity, divisibility, systematicity and market selectivity, and companies may generate technological investment expansion and form technological job demand, which may also lead to the occupation of company resources and trigger the replacement of the original jobs. This paper empirically examines the impact of technological advance in industry on the labour employment demand of companies by taking Chinese A-share listed companies as research samples from 2008 to 2022, and finds that: overall technological advance in industry has a suppressive effect on the labour employment demand of companies; The heterogeneity test shows that industrial technological advancement mainly produces inhibitory effects on the labour employment of low-education level employees and production sectors, while it produces incentive effects on the labour employment of high-education level employees and non-production sectors Industrial technological advancement mainly produces job substitution and destructive effects on the labour employment of low-education level employees and production sectors, while it produces incentive effects on the labour employment of high-education level employees and non-production sectors. Mechanism test shows that industrial technological advancement has incentive effects such as technology investment expansion effect and industry chain conduction effect, and also produces inhibitory effects such as enterprise resource occupation effect and employment delay effect. This paper extends the research on the impact of industrial technological advances on the labour employment demand of companies, and provides empirical evidence and policy insights for rationally arranging industrial structural transformation and labour employment decisions of companies in the context of 'stable employment'.

Neurophysiological mechanisms underlying the influence of motivational intensity on emotion regulation choice.

Motivation is considered a crucial factor in determining emotion regulation choice (ER choice). Our previous study revealed that increased motivation led to a preference for distraction over reappraisal due to the narrowed attention, yet neural underpinnings supporting this phenomenon remain unclear. In this study, we used event-related potentials (ERPs) to investigate whether motivation influences ER choice by early attention processing (P2) or late resource engagement (LPP). EEG activities were recorded for the pictorial contexts varying in motivational intensity and direction, while participants (N = 68) were required to decide which strategy from the two options they would use to regulate their emotions. Consistent with previous studies, the results indicated that individuals chose more distraction over reappraisal in high-withdrawal motivated contexts while a reversed pattern was observed in low-withdrawal motivated contexts. Moreover, high-intensity motivated emotional contexts result in enhanced P2 and LPP. Further analysis indicates that only frontal P2 and the early LPP mediate the effect of motivational intensity on ER choice. These findings suggest that heightened motivation leads to a preference for distraction over reappraisal during emotion regulation due to the greater occupation of attentional resources.

Does every public open space (POS) contribute to sustainable city development? An assessment of inefficient POS in Beijing

Public open space (POS) plays a crucial role in influencing the urban environment, ecology, public health, and economic vitality. However, not every POS is being efficiently utilized, while the inefficient POSs occupy large areas and can negatively affect socio-economic development, thus posing a threat to sustainable city development. Existing studies evaluate POSs solely based on benefit outcomes, but ignore POS functional types and contextual environments. To address this issue, a categorized, integrated, and relative evaluation method, i.e., utilization efficiency (UE), is proposed to identify inefficient POSs, where essentially measures the ratio from benefit outcome to resource occupation of each POS. We firstly classify POSs into five types by considering their core functions, then measure their UEs to extract efficient and inefficient POS, and finally visualize key causes of different types of POS. Our results reveal 3,138 inefficient out of 10,943 POSs in Beijing, and show the inefficient POSs that are concentrated in the southern Beijing and northern regions between the 5th and 6th ring roads, mainly affected by local population density and environmental resource. Drawing on the features of inefficient POSs, it is suggested to increase small green spaces and sky gardens in the downtown areas, improve service facilities and basic tourism infrastructure in the suburbs. Accordingly, this study firstly incorporates POS functional types and resource occupation into the utilization efficiency evaluation, providing spatial optimization recommendations of POSs to facilitate sustainable city development.

Exploring the Socio-economic Conditions of Potato Farmers in Farrukhabad District of Uttar Pradesh, India

This study investigates the socio-economic profile of potato growers in Farrukhabad district, Uttar Pradesh, aiming to understand their demographic composition, educational background, caste distribution, marital status, family structure, landholding size, occupation, income levels, social participation, and possession of farming resources. A sample of 100 respondents cultivating potatoes as the main crop was selected from Kamalganj block, Farrukhabad district. Data were collected through personal interviews using a pre-structured schedule and analyzed using percentages. The results reveal that the majority of respondents are middle-aged, literate, and belong to scheduled castes. Most respondents are married and come from joint families with medium-sized households. Marginal and small landholdings dominate, and agriculture is the primary occupation, supplemented by subsidiary activities such as dairying and business. Income levels vary, with a significant proportion falling within the medium-income bracket. Social participation is moderate, with most respondents engaging in multiple organizations. Farm resources such as farm power and implements are moderately possessed. Extension services and information sources play a crucial role, with most respondents exhibiting moderate contact. This study provides valuable insights into the socio-economic landscape influencing agricultural practices among potato growers, which can inform policy formulation and extension activities aimed at fostering agricultural development and rural prosperity.

Study on the Deviation Characteristics of Driving Trajectories for Autonomous Vehicles and the Design of Dedicated Lane Widths

The vehicular trajectory offset represents a critical controlling element in the design of lane width. In light of the paucity of extant research on the lane widths for dedicated autonomous vehicle lanes, this study deployed the PreScan-Simulink co-simulation platform. Based on the established typical lateral and longitudinal control methods for autonomous vehicles, we initially identified the primary factors influencing trajectory offset through multifactorial coupled analysis. Subsequently, we conducted quantitative research on vehicle trajectory offset using S-shaped curves to elucidate the patterns in geometric elements’ impact on trajectory offset. Following this, we established a model of the relationship between design speed and trajectory offset under different vehicle types. Ultimately, we calculated the lane width values for scenarios involving varying positions and numbers of dedicated lanes. The results demonstrate that vehicle speed significantly impacts the trajectory offsets of autonomous vehicles. For passenger cars, the mean offset at speeds between 60 and 130 km/h is approximately 10 cm. At higher speeds of 140–150 km/h, the offset is more variable. The range of offset exhibited by trucks at speeds between 60 and 100 km/h is [8 cm, 16 cm]. In the case of a single dedicated lane, the width of the inner lanes intended for passenger cars is [2.60 m, 3.00 m], while the outer lanes designed to accommodate trucks have a width of [3.00 m, 3.20 m]. In scenarios with two dedicated lanes, the width of lanes for passenger cars can be reduced further, whereas the required lane width for trucks remains largely unchanged compared to that for single-lane setups. The conclusions show that the width of lanes adapted to autonomous vehicles could be reduced, which could help to optimize the use of road space, thus potentially reducing the occupation of land resources, reducing the environmental impact of road construction, and contributing to sustainable development. This study also provides valuable insights for the design of lanes dedicated to autonomous vehicles.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

A practical vehicular‐fog‐based privacy‐preserving attribute‐based keyword search system using potential games

AbstractThe growth of fog computing drives Internet of Things users to outsource data for services like storage and sharing. To ensure secure and efficient data search, extensive research has been conducted on searchable encryption, specifically attribute‐based keyword search (ABKS). ABKS facilitates precise access control over encrypted data while maintaining search functionality. However, delegating complex search operations to fog nodes introduces challenges like latency limitations, compromised search results, and unauthorized occupation of server resources. In this manuscript, we propose a Time‐Efficient Data Retrieval (TEDR) scheme to address these challenges. Our scheme incorporates two access policies, allowing data owners (DOs) and data users (DUs) to control data access and search queries. TEDR enables fog nodes to verify search queries before decryption, avoiding unnecessary decryption for invalid queries. We introduce a decentralized query submitting framework to allocate search queries among fog nodes, addressing redundancy concerns and latency issues. We analyze the decentralized query submitting game and classify it as a potential game, making it suitable for decentralized decision‐making. We evaluate TEDR performance on real‐world datasets, demonstrating practicality and efficiency. Our scheme reduces computational and communication costs by approximately 30% compared to existing methods.

Meta-Analysis on the Impact of Different Channel Loads on Takeover Performance in Autonomous Driving under Non-Driving Tasks

Abstract Under conditions of high automation, drivers’ excessive engagement in non-driving related tasks can severely impact their takeover ability, posing a significant threat to road safety. Therefore, it’s essential to explore the channel resources occupied by non-driving related tasks to enhance driving safety. However, many studies have merely categorized different types of tasks in experiments without clarifying the relationship between the tasks and the channel resources they occupy. To determine whether different channel loads under non-driving related tasks have varying impacts on autonomous driving takeover performance, a meta-analysis was conducted on literature retrieved from January 2021 to January 2024. The results indicate that tasks occupying visual, auditory, motor, and cognitive channel resources during takeover all affect takeover performance. Among these, cognitive channel resource occupation significantly reduces takeover efficiency, followed by motor and auditory channels, with the visual channel having the least impact.

Performance Testing of the Triple Modular Redundancy Mitigation Circuit Test Environment Implementation in Field Programmable Gate Array Structures

The logic structures implemented in Field Programmable Gate Arrays (FPGAs) are often critical and their correct operation is vital. FPGA devices are often used in areas where there is increased ionising radiation (space, medical diagnostics, aviation or nuclear power). There is therefore a need for mechanisms to correct radiation-induced errors. A common approach is the redundant implementation of particularly critical parts of the logic structure. By triplicating selected fragments, it is possible not only to detect potential errors but also to correct them. Such an approach is called triple modular redundancy (TMR), and its essence lies in the use of specialised voting circuits called voters, which allow the erroneous results of individual subcircuits to be eliminated by voting. The triplicate circuit under consideration, together with the voter, constitutes the mitigation structure. It becomes necessary to develop a test environment to assess the correct operation of these circuits. Also key is the efficiency of the implementation of these structures, which can be related to the occupation of logical resources or the power consumption of a given implementation. This paper demonstrates the essence of implementing a test environment to test the correctness of the mitigation of logic structures using TMR voters. An error injector mechanism using the Pseudo-Random Bit Sequence (PRBS) register is proposed, which introduces an element of randomness into the testing process. The aim of this research is to determine the implementation efficiency of the proposed test environment. In the experimental part, the implementation costs of the proposed solution were examined. The results indicate that between 66 and 109 LUT blocks were required to implement the error injector, corresponding to a relatively small increase in dynamic power consumption: by 22% for combinational circuits and by 37% for sequential circuits.

Asynchronous Memory Access Unit: Exploiting Massive Parallelism for Far Memory Access

The growing memory demands of modern applications have driven the adoption of far memory technologies in data centers to provide cost-effective, high-capacity memory solutions. However, far memory presents new performance challenges because its access latencies are significantly longer and more variable than local DRAM. For applications to achieve acceptable performance on far memory, a high degree of memory-level parallelism (MLP) is needed to tolerate the long access latency. While modern out-of-order processors are capable of exploiting a certain degree of MLP, they are constrained by resource limitations and hardware complexity. The key obstacle is the synchronous memory access semantics of traditional load/store instructions, which occupy critical hardware resources for a long time. The longer far memory latencies exacerbate this limitation. This article proposes a set of Asynchronous Memory Access Instructions (AMI) and its supporting function unit, Asynchronous Memory Access Unit (AMU), inside contemporary Out-of-Order Core. AMI separates memory request issuing from response handling to reduce resource occupation. Additionally, AMU architecture supports up to several hundreds of asynchronous memory requests through re-purposing a portion of L2 Cache as scratchpad memory (SPM) to provide sufficient temporal storage. Together with a coroutine-based programming framework, this scheme can achieve significantly higher MLP for hiding far memory latencies. Evaluation with a cycle-accurate simulation shows AMI achieves 2.42× speedup on average for memory-bound benchmarks with 1μs additional far memory latency. Over 130 outstanding requests are supported with 26.86× speedup for GUPS (random access) with 5 μs latency. These demonstrate how the techniques tackle far memory performance impacts through explicit MLP expression and latency adaptation.

Model-free aperiodic tracking for discrete-time systems using hierarchical reinforcement learning

In the operation of practical systems, it is often a challenging task to deal with limited knowledge of the system dynamics, therefore, it is needed to obtain a framework for the simultaneous learning and control of dynamic systems, able to cope with uncertain dynamics. This paper proposes a model-free aperiodic tracking control method based on MAXQ hierarchical reinforcement learning for unknown dynamics in discrete-time systems. Firstly, a MAXQ hierarchical framework is developed for aperiodic tracking control that decomposes the task into pre-control and accurate control subtasks. The former achieves sub-optimal tracking which prompts the implementation of accurate control. The latter implements aperiodic tracking based on the pre-control result to reduce resource occupation. The structure of the MAXQ framework simplifies the complexity of the tracking task, and the incorporation of pre-control accelerates the learning process in the formal control stage. Secondly, considering the disparities between the control inputs and the control update instants, pre-control is decomposed to learn the triggering strategy and control policy, respectively. Meanwhile, the control policy learns optimal control inputs by minimizing the cost function. Similarly, the triggering strategy and control policy are also separately learned in accurate control. In contrast to traditional aperiodic triggering mechanisms, in accurate control stage, the triggering strategy is developed based on the cumulative error of the pre-control result, thus avoiding the influence of accidental factors and the cumulative effects of errors, enhancing system robustness. Thirdly, the proposed tracking control is derived by using only the input, output, and reference signal data from the system, without relying on system dynamics. It is applicable to both linear and nonlinear systems, demonstrating strong generalizability. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed method.

Miniaturized photoelectrochemical sensing system for reusable detection of macromolecules and its applications for unattended environmental monitoring

Photoelectrochemical (PEC) sensors are widely employed in biochemical detection due to their accuracy and speed, but most PEC measurement systems face challenges with bulky instruments and complex operations. Current miniaturized systems based on Potentiostat and constant light are limited in accuracy and application ranges. PEC systems based on mod/demod signal processing offer high SNR and accuracy but struggles with miniaturization due to complex algorithms of Lock-in amplifier. Herein, discrete Fourier transform (DFT) algorithm instead of Lock-in algorithm was used for the photocurrent demodulation in PEC system. After functional verification, coordinate rotation digital computer (CORDIC) algorithm was used to implement DFT demodulation to further reduce resource occupancy. Afterwards, the spectral leakage was reduced by applying Hanning window, which improves the SNR and accuracy of photocurrent demodulation. Subsequently, a miniaturized, low-cost, low-power and high-accuracy PEC measurement system was obtained by integrating designed Hanning-CORDIC-DFT module, Potentiostat, light source driver and other miniaturized modules. The performances of miniaturized PEC measurement system were compared with that of the commercial PEC measurement system based on Lock-in amplifier by using the same MIP-PEC sensor, validating the feasibility and accuracy of designed miniaturized PEC system. Then, the performance of MIP-PEC sensor was investigated in detail. The results showed that MIP-PEC sensor has good sensitivity, stability, selectivity and recyclability for detecting MC-LR. The reliability of MIP-PEC sensor was further confirmed by the recovery test of MC-LR in lake water. Finally, long-term, cyclic and unattended measurements of microcystin-LR were achieved by designed miniaturized PEC system and MIP-PEC sensor under different conditions, verifying the practicality of the system for field monitoring. The designed measurement system provides a new method for constructing miniaturized PEC sensing platform, and a new solution for continuous biochemical detection in complex environments.

Image-free Hu invariant moment measurement by single-pixel detection

The global feature invariant moment plays a crucial role in capturing the feature information of a target. At present, the extraction of the target moment depends on the processing of the two-dimensional image, which leads to the high computational resource occupation due to the large amount of inherent data and the high information redundancy of the image. This reduces the real-time performance of target moment extraction to a certain extent. In order to solve this problem, we introduce a novel strategy for extracting Hu invariant moments, which is based on the combination of single-pixel detection technology and the Hu invariant moments theory. To the best of our knowledge, this is the first proposal of an image-free extraction strategy employing single-pixel detection. The extraction of moments is seamlessly integrated into the single-pixel detection process, facilitating efficient and rapid acquisition, followed by the construction of Hu invariant moments. Theoretical analysis and experimental validation of our proposed method are carried out, demonstrating the effective acquisition of Hu invariant moments. This technology holds substantial application value and prospects in various fields, including target analysis, understanding, classification, and recognition.

Estimating the building heights and occupancy of underground space resources based on high-resolution remote sensing images

ABSTRACT In recent years, limited surface space and restricted urban expansion have become increasingly prominent. The development of underground spaces has emerged as an effective solution, offering a new dimension for urban growth and sustainability. However, due to the non-renewable and costly nature of underground space (UGS), it should be properly planned before development to avoid waste of resources and economic loss. In this study, the potential of UGS development based on the influence of surface buildings was evaluated by using the high-resolution satellite images of Gaofen-2 (GF-2) in Qingdao, Shandong Province, China. Firstly, the DeepLabv3+ network was lightened to enhance the training efficiency and building extraction accuracy. The building shadows were then extracted by an improved shadow measurement model, and used to estimate the height of extracted buildings based on the imaging mechanism and information of the remote sensing image. Finally, the building height (BH) was converted into the depth of influence on the UGS, and the development potential of the UGS was then estimated. The experimental results show that the UGS within 0–10 m in Qingdao city has been extensively occupied. However, the geological layers between 10–30 m and 30–50 m contain large continuous areas that have not been utilized, indicating the greatest potential for resource development within the city.

An Intelligent Cockpit Tailored Carpet for Human‐Vehicle Interaction Enhancement and Driving Intention Recognition

AbstractIn the realm of intelligent cockpits, the predominant emphasis on enhancing driving safety and comfort through human‐vehicle interaction and driving behavior monitoring has conventionally centered on the upper body of the driver. Regrettably, the wealth of information inherent in the lower half of the driver's body, particularly the feet, tends to be systematically underestimated by researchers. A specialized intelligent carpet tailored for automotive cockpits that integrates sponge‐based triboelectric sensor to monitor driver's foot movement is proposed. The intelligent carpet contains two areas, namely the human‐vehicle interaction sensing area and the driving intention monitoring area. With the human‐vehicle interaction sensing area, the driver can interact with the vehicle through the left foot, resulting in reducing the occupation of visual resources and hand load. Furthermore, the driving intention monitoring area supported by the right foot can effectively identify driver's intentions and driver's behaviors, which are essential to trajectory prediction, optimal path planning, and driving safety. It is demonstrated that an intelligent carpet is potentially useful for human‐vehicle interaction, and transportation optimization and accident prevention.

Deep learning model of semantic direction exploration based on English V+able corpus distribution and semantic roles

In order to improve English learning efficiency, this paper constructs a deep learning model of semantic orientation exploration based on English V+able corpus distribution and semantic roles. This article combines the practical needs of English learning and establishes an ILP model with the optimization objective of minimizing spectrum resource occupation. A traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed to solve the standardization problem of English speech recognition. To improve the system efficiency of intelligent English learning systems, a traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed. Through research, it has been verified that the semantic orientation exploration deep learning model based on the distribution of semantic roles in English V+able corpora can effectively improve the effectiveness of English speech learning. The corpus model proposed in this paper can provide a reliable benchmark database for many speech problem learners and play an important role in English translation software in recognizing input speech with different accents

A Survey on Maximum Ratio Combination: Applications, Evaluation and Future Directions

With the rapid development of wireless communications, the occupation of time and frequency resources becomes more crowded. The exploitation of space resources is necessary and the diversity combining techniques have substantial applications. Diversity combining achieves great diversity gains and improves the ability to combat multipath fading, among which the maximum ratio combining (MRC) performs as the optimal linear combining approach. However, MRC suffers from detrimental factors such as channel fading and no Gaussian noise in practical scenarios. In this paper, we focus on a comprehensive investigation of MRC. Starting from the MRC principle and system model, we summarize typical scenarios and analyze the channel fading statistics. For the influential factors, we further review related literature on channel correlation, cochannel interference (CCI) and impulsive noise. Major performance criteria and performance bounds are derived and compared. MRC confronts new developing challenges and the major development directions are reviewed. The paper finally discusses recent works and open problems for MRC applications and development. Emerging techniques such as artificial intelligence provide novel solutions for MRC performance improvements. The paper aims to present a summarized insight to assist readers in clarifying the analyzed methodology of MRC, so as to motivate new technology integration and extensive applications of advanced communication systems.

Chaos theory meets deep learning: A new approach to time series forecasting

We explore the influence and advantages of integrating chaotic systems with deep learning for time series forecasting in this paper. It proposes a novel deep learning method based on the Chen system, which leverages the randomness, sensitivity, and diversity of chaotic mapping to enhance the performance and efficiency of deep learning models. We introduce a deep learning framework that integrates chaotic systems, providing an innovative and effective approach for time series forecasting. The research utilizes three different types of deep learning models as baselines—Long Short-Term Memory, Neural Basis Expansion Analysis, and Transformer—and compares them with their chaotic counterparts to demonstrate the impact of chaotic systems on various deep learning architectures. Experimental validation is conducted on thirteen available time series datasets, assessing the models’ forecasting accuracy, runtime, and resource occupancy. The effectiveness and superiority of the chaotic deep learning method are verified across diverse datasets, including stock markets, electricity, and air quality, showcasing significant improvements over traditional model initialization methods.

Analysis on Fractal-Permeability Characteristics of Construction Waste Filling Body in Gob

The mine application of construction waste permeability filling body is an effective way to avoid the collapse of overlying rock strata caused by coal mining, to solve the questions of surface resource occupation and pollute the ecological environment, and to promote regional hydrogeological balance. Based on the infiltration test on twelve groups of non-cemented and cemented samples with different particle size ratio, the variation characteristics of permeability coefficient of non-cemented and cemented construction waste samples with different fine content and cement were analyzed. The porosity of non-cemented samples and cemented ones was obtained, the relationships of fine content and porosity, permeability coefficient were analyzed, the relationships of fractal dimension and porosity, permeability coefficient on non-cemented samples and cemented ones were discussed. The results showed that fine content was negatively correlated to the permeability coefficient of non-cemented samples and non-cemented ones. The porosity decreased with fine content increasing, the porosity of non-cemented samples ranged from 5.007~7.029%, and the one of cemented samples was 3.230~6.508%. Fractal dimension of non-cemented samples and cemented ones were fine aggregate> medium coarse aggregate> coarse aggregate, and the fractal dimension of non-cemented samples was less than the cemented ones, the porosity and the mass fractal dimension could well represent the relationship between the permeability and fine content.