Critical Technology Research Articles

Batched-image detection model and deployment method for tunnel lining defects using line-scan cameras based on experiments study

High-performance line-scan cameras are highly accurate and efficient for tunnel detection, however, the exponential growth of image data creates new challenges for real-time image processing. The inference speed of existing tunnel detection algorithms is insufficient for massive amounts of image data, and traditional data structures limit the parallelism of the detection system, thereby reducing the speed of image processing. In this study, a deep learning (DL) network is developed for real-time tunnel defect detection based on the you only look once (YOLO) family. The network includes innovative defect feature-extraction modules, optimization of the network architecture, and improvement of the decoupling heads to increase defect detection accuracy and efficiency. Furthermore, we developed critical technologies for real-time detection. First, our method employs a deployment method for DL networks based on TensorRT, which enables our network to be applied directly to C++ image acquisition programs away from the traditional DL frameworks. The proposed line-scan data structure enables the raw images to be fed into the neural network in batches, which, combined with the simultaneous technique of image acquisition, detection, and storage, significantly increases the efficiency of real-time detection. The paper demonstrates the advantages of the proposed model and hardware system through an experiment study. The test results indicate that the mean average precision and F1-score of our network were 86.07% and 84.53%, respectively, while it had 21.42 M parameters. Combined with the proposed storage strategy, a recall rate of more than 90% was achieved for defect detection. Moreover, batch image processing method has a significant advantage in terms of inference speed, with 243.9 FPS in PyTorch, 381.46 FPS in TensorRT, and 305.3 FPS in real-time detection, which is five times faster than existing defect-detection models. This study significantly increases the efficiency of tunnel lining detection, which has positive implications for the visual detection of other infrastructures.

Public perceived risks and benefits of carbon capture, utilization, and storage (CCUS): Scale development and validation

As a critical technology to mitigate climate change, the large-scale implementation of carbon capture, utilization, and storage (CCUS) depends on both technological advancement and public acceptance, which is significantly influenced by the perceived risks and benefits. Existing studies, however, have yet to reach a consensus regarding the measurement of CCUS in these two aspects. To fill this gap, this paper develops and validates new scales based on four studies. Specifically, Study 1 generates the initial item pool based on a literature review and semi-structured interviews, and then invites experts to examine the content validity of these items; Study 2 identifies the dimensions and assesses the reliability and validity of the measures through an exploratory and confirmatory factor analysis; Study 3 conducts a one-way ANOVA to test known-group validity; and Study 4 employed structural equation modeling to evaluate the nomological validity. The results demonstrate the internal consistency, reliability, and construct validity of the new scales developed to measure CCUS. This study provides a valuable tool for investigating public perceptions of CCUS and can help policymakers develop future publicity strategies.

Intelligent Learning Algorithm and Intelligent Transportation-Based Energy Management Strategies for Hybrid Electric Vehicles: A Review

As one of the alternatives to conventional fuel vehicles, hybrid electric vehicles (HEV) offer lower fuel consumption and fewer exhaust emissions. To improve the performance of the HEV, the energy management strategy (EMS) is one of the most critical technologies. Classic EMS can be broadly classified into rule-based and optimization-based. With the development of machine learning technology, the deep reinforcement learning (DRL) algorithm of intelligent learning algorithms has been applied to the EMS. This paper mainly reviews the research progress of the EMS based on DRL from two aspects of the algorithm and training environment, and the EMS research involving combining the intelligent transportation system (ITS) is reviewed. In addition, the experimental test progress situations of DRL-based EMS research are discussed. Finally, the challenge of DRL-based EMSs is analyzed and some solutions are provided. In particular, it also involves some discussion about automotive cyber security in the intelligent transportation environment.

The usage of probiotic microorganisms in production technology of European grayling fish stock

European grayling is a perspective object of a recreational fishing. In addition, the presence of this species in sufficient quantities in rivers has a positive influence on the formation of the recreational attractiveness of mountain regions, where tourism is one of the main sources of local communities existence. However, grayling stocks in the watercourses of Ukraine are significantly depleted nowadays, and populations are in a condition of lack, what has led to the inclusion of this species into the Red Book of Ukraine. Working out of biotechnologies of artificial reproduction of rare fish species in aquaculture conditions for the further reintroduction of the resulting offspring into natural water bodies takes an important place in the system of environmental protection measures to preserve biodiversity. After all, the reintroduction into nature of artificially obtained fish fry is one of the effective ways to restore the optimal number and structure of rare fish species natural populations, and this, in turn, will allow in the future to restore the limited exploitation of the stock of these fish species. One of the critical artificial fish reproduction technology links is the transfer of larvae to external nutrition. Namely on this stage the highest mortality rates are observed. The usage of live feeds as a starter fodder can significantly improve the situation. Forage organisms, in addition to having high nutritional value, can serve as delivering tools of various targeted products, including probiotics, to a fish larvae organism. The assessment of the usage of probiotic strain Lactobacillus casei IMV B-7280 feasibility is carried, and also the possibility of usage the culture Daphnia magna Straus, 1820 grown in the presence of trehalose lipids, within the technology of early feeding of the European grayling Thymallus thymallus (Linnaeus, 1758). It was shown that the larvae of the European grayling, which received investigated probiotic microorganisms strain, had 15% bigger weight on the final stages of feeding than the larvae of the control group. Introduction to the diet of grown-up grayling larvae of the culture Daphnia magna, previously received of common cultivation with microalgae Desmodesmus armatus (Chodat) E.H.Hegewald 2000 with addition of trehalose lipids, contributed to the reduction of larval mortality and the acceleration of their growth rate. Further improvement of proposed approaches of growing grayling fish planting material can be extrapolated to other fish species and in the future may be applicable in the process of growing rare fish species in conditions of farm aquaculture fish farms, what will allow them to be involved into environmental protection activities by placing appropriate orders. This, in its turn, will contribute to the stable development of communities in rural areas.

Influence of physical-property variations on leakage for supercritical CO2 seal

In the supercritical CO2 Brayton cycle, dynamic sealing of rotating machines is one of the critical technologies. Under drastic variations of physical properties, the flow characteristics, sensitivity of parameters to leakage, and feasibility of theoretical models are not well understood. Experimental parameters of labyrinth seals include upstream parameters (302.15 K to 379.15 K, 7 MPa to 14 MPa) and pressure ratios (1.05 to 1.60, below the critical flow) covering trans-critical and pseudo-critical conditions. A comparison between experiments and traditional models leads to the following conclusions: In terms of linear correlation with the leakage, the upstream parameters in the density form are significantly superior to those in the temperature form. For the expansion factor part, the isentropic form can lead to errors ranging from -25% to 45%. In model prediction, the use of both temperature-based and isentropic-based parameters can result in significant prediction errors ranging from -41% to 67%. This paper recommends Martin's model with the density-based upstream parameters, the iterative isentropic model, and the Childs model, each with an error of ±10% or less.

MagNet—A Data‐Science Competition to Predict Disturbance Storm‐Time Index (Dst) From Solar Wind Data

AbstractEnhanced interaction between solar‐wind and Earth's magnetosphere can cause space weather and geomagnetic storms that have the potential to damage critical technologies, such as magnetic navigation, radio communications, and power grids. The severity of a geomagnetic storm is measured using the disturbance‐storm‐time (Dst) index. The Dst index is calculated by averaging the horizontal component of the magnetic field observed at four near‐equatorial observatories and is used to drive geomagnetic disturbance models. As a key specification of the magnetospheric dynamics, the Dst index is used to drive geomagnetic disturbance models such as the High Definition Geomagnetic Model—Real Time. Since 1975, forecasting models have been proposed to forecast Dst solely from solar wind observations at the Lagrangian‐1 position. However, while the recent Machine‐Learning (ML) models generally perform better than other approaches, many are unsuitable for operational use. Recent exponential growth in data‐science research and the democratization of ML tools have opened up the possibility of crowd‐sourcing specific problem‐solving tasks with clear constraints and evaluation metrics. To this end, National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information and the University of Colorado's Cooperative Institute for Research in Environmental Sciences conducted an open data‐science challenge called “MagNet: Model the Geomagnetic Field.” The challenge attracted 622 participants, resulting in 1,197 model submissions that used various ML approaches. The top models that met the evaluation criteria are operationally viable and retrainable and suitable for NOAA's operational needs. The paper summarizes the competition results and lessons learned.

Empirical Transfer Function Estimation With Differential Filtering and Its Application to Fine Positioning Control of Galvano Scanner

Fast and precise point-to-point (PTP) positioning control is a critical technology for improving the throughput and product quality of industrial machines. This study aims to investigate the frequency response function (FRF) estimation method with differential filtering (ETFE-Diff: empirical transfer function estimation with differential filtering) that uses the input and output signals of PTP motion during processing operation and to apply ETFE-Diff to a feedforward (FF) compensation adjustment. In this study, the principle of ETFE-Diff for FRF estimation was clarified, compared to the existing FRF estimation methods, i.e., empirical transfer function estimation and local rational modeling. Moreover, the effectiveness of ETFE-Diff for FF compensation adjustment in improving positioning accuracy of a galvano scanner as an industrial servo system was experimentally demonstrated.

The Use and Non-Use of Technology During Hurricanes

Hurricanes can cause catastrophic damage; it is critical for those affected to access information about conditions, loved ones, and resources. Prior work in the HCI and CSCW communities has focused on how social media can be vital during natural disasters; non-social media technologies have been under-researched. To understand how technology other than social media can support or harm people during crises, we explore hurricane survivors' use and disuse of multiple kinds of technologies in online surveys with 138 US participants. We find substantial technology use supporting survivors' comfort and safety other than social media. We also observe that designing technologies for high-resource environments--as with many mainstream apps--causes users to decrease use of potentially critical technologies during utility outages, which are common during hurricanes. With themes of both (a) broad technology use and (b) conditions preventing technology use, we make recommendations for technical design, policy, and research to empower communities susceptible to hurricanes.

DIG-SLAM: an accurate RGB-D SLAM based on instance segmentation and geometric clustering for dynamic indoor scenes

Simultaneous localization and mapping (SLAM) has emerged as a critical technology enabling robots to navigate in unknown environments, drawing extensive attention within the robotics research community. However, traditional visual SLAM ignores the presence of dynamic objects in indoor scenes, and dynamic point features of dynamic objects can lead to incorrect data correlation, making the traditional visual SLAM is difficult to accurately estimate the camera’s pose when the objects in the scenes are moving. Using only point features cannot fully extract geometric information in dynamic indoor scenes, reducing the system’s robustness. To solve this problem, we develop a RGB-D SLAM system called DIG-SLAM. Firstly, the objects’ contour regions are extracted using the YOLOv7 instance segmentation method, serving as a prerequisite for determining dynamic objects and constructing a semantic information map. Meanwhile, the line features are extracted using the line segment detector algorithm, and the redundant line features are optimized via K-means clustering. Secondly, moving consistency checks combined with instance partitioning determine dynamic regions, and the point and line features of the dynamic regions are removed. Finally, the combination of static line features and point features optimizes the camera pose. Meanwhile, a static semantic octree map is created to provide richer and higher-level scene understanding and perception capabilities for robots or autonomous systems. The experimental results on the Technische Universität München dataset show that the average absolute trajectory error of the developed DIG-SLAM is reduced by 28.68% compared with the dynamic semantic SLAM. Compared with other dynamic SLAM methods, the proposed system shows better camera pose estimation accuracy and system’s robustness in dynamic indoor environments and better map building in real indoor scenes.

The dynamics of innovation in CoPS industries: evidence from the Brazilian aerospace industry

ABSTRACT This article frames the central role played by mission-oriented public procurement in the Brazilian aerospace industry. The difficulties in mastering and promoting the evolution of several disciplines at the firm level, with a relative scarcity of funds for an R&D-intensive industry and an incomplete national innovation system represent merely a fraction of the threat faced by those dealing with Complex Products and Systems. The culture of financing flagship projects using public procurement narrows the potential solutions for complex problems, imposes time constraints to mature critical technologies and exposes these endeavours to high levels of risk. By taking the Dynamic Capabilities as a theoretical framework and Technological Readiness Level as a yardstick to the decision-making process, we have developed a construct to explore two in-depth cases. A cultural shift is required, aiming at providing the means to the progressive maturation of critical technologies using public procurement for innovation.

Key technologies of active distribution networks control

Active Distribution Networks (ADNs) are modern and intelligent power distribution systems that integrate renewable energy sources and advanced control technologies to provide reliable, secure, and efficient electricity supply. The traditional power distribution networks are designed to deliver electricity in a one-way flow from the transmission system to the customers. In contrast, ADN enables two-way power flow between the distribution network and customers, allowing them to become active participants in the electricity system. The key technology behind ADN includes advanced metering infrastructure (AMI), Distribution Automation (DA), and energy storage systems (ESS). AMI provides real-time data on electricity consumption, generation, and distribution, enabling efficient load management and demand response. Other key technologies of ADN include renewable energy sources such as solar photovoltaic (PV), Wind turbines and small-scale hydroelectricity. These sources are connected to the distribution network through power electronic interfaces that convert the DC output of the renewable sources into AC for distribution. Smart grid technologies such as microgrids, virtual power plants, and energy management systems are also integrated into ADN to improve grid stability and resilience. ADNs have the potential to transform the electricity system by enabling more efficient, sustainable, and reliable energy systems that can better serve the needs of consumers and society. Thus, ADN is expected to become a critical technology in achieving a cleaner and more sustainable energy future.

Reliability optimization in IRS-assisted UAV networks

Unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) are regarded as critical technologies which need to be developed for the sixth-generation mobile network (6G). In this paper, we study the reliability of the IRS-assisted Unmanned aerial vehicles (UAV) communication system with limited UAV energy in complex urban scenarios. We present the problem by joint optimization of the IRS's scheduling, the UAV's trajectory, the IRS's phase shift, and the UAV's transmit power for maximizing the reliability of the transmission link under the constraints of UAV energy and channel error rate. The concavity of the problem is difficult to judge, and the problem is hard to find a substitution function to convert it into a tractable form. Therefore, we design a chaotic adaptative hybrid whale optimization algorithm (CAHWOA) to address the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the variational perturbation binary whale optimization algorithm (VPBWOA). CAWOA and VPBWOA are used for solving the continuous variable subproblem and the binary variable subproblem, respectively. The numerical results show that the joint optimization of IRS and UAV improves the transmission link reliability by nearly 30% compared with the two baseline schemes. CAHWOA can improve the convergence rate by about 18% compared with the baseline algorithms. Meanwhile, it can enhance the optimization-seeking accuracy by about 0.04.

The digital divide: David vs. Goliath.

There is a global danger that hides in the shadows and is changing the way continuing care operators care for residents. Today, to provide that same historical level of resident care, a group of techno-soldiers are not just a nice-to-have, but are a must, to protect our critical Information Technology (IT) infrastructure. There is little doubt that technology has enabled us to provide better care. Electronic medical records, human resources information systems, and voice over internet communications systems have created great efficiencies in operations. However, global cybercriminals lurk in the same technologies that support us. Continuously attempting to rob our private information and hold us ransom, or play havoc with our IT systems impeding our provision of care. How can a continuing care operator, an already funding-challenged industry balance the costs of protecting their IT infrastructure against this growing massive threat? Compounding the issue, rising insurance expectations for continuing care operators to continuously enhance their IT safety controls against evolving cyberterrorism. This is the story of the Good Samaritan Society/Canada (Good Samaritan) digital transformation journey. Our digital roadmap sets Good Samaritan up to rise above the ongoing barrage of cyberattacks and loss of insurance. This is a digital divide story of David vs. Goliath.

Alternations of hardness and tensile properties of the ultrasound-assisted aging treatments on the Al-Mg-Si alloy

Lightweight metals with appropriate strength are crucial materials among the critical future technologies and sustainable issues. The Al-Mg-Si alloys, which are processed by aging treatments, are considered promising candidates. Hence, the pseudo-binary Al-Mg2Si alloy with an excess Si addition was chosen in this study. In addition, to further manipulate the alloys, besides to the often-controlled two factors of aging temperature and aging time, ultrasound was introduced into the aging system as an additional third factor to further control the aging behaviors. One-step and two-step aging treatments were both conducted to verify the effect of the introduction of ultrasound. It was found that certain alternations of mechanical properties, such as micro Vickers hardness, which is a surface property, were obviously imposed. On the other hand, the tensile strength and fracture strain, which are bulk mechanical properties, showed limited changes between the ultrasound-applied and ultrasound-free specimens. Many possibilities have been proposed and discussed for the observed phenomenon. The cavitation would be the greatest factor in the alternation of the micro Vickers hardness. Microstructures observed by bright-field images were in good accordance with that of the observed mechanical properties.

Exploring Vehicle Security Improvement Framework in the Safety Messaging System to Protect Against Hackers

The transportation and automotive industry have seen the shifts and effects of innovation within the last decade, from the addition of critical messaging technologies, event response systems, to safety integration of each user initiated by safety messaging systems to almost all private utility vehicles to a public utility vehicle, all have seen either responsive or unresponsive layers of usability and functionality issues, together with the need for safety and protection of each users and riders. These assistive technologies are supposed to help drivers and operators in their daily driving routine, believed to be connected to a myriad of networked hardware and software, and arguably to be either be cost effective or safety limited. The study exploring vehicle security improvement framework in the safety messaging system to protect against hackers delivered responses from 6 subject matter experts within the public transit authority, cloud supply chain companies, and network providers that have invested greatly in vehicle ad hoc network technologies. Thematic analysis was chosen as a qualitative method design to extract themes from the critical answers through the semi-structured interview retrieved from general responses from the subject matter experts. The general findings were extracted from the major themes 1 through 5. The general findings are the following (1) Automotive Organizations and Transit Authority Leaders have to Manage Vehicle Cyber Risks Throughout the Vehicle Lifecycle, (2) Automotive Organization and Transit Authority Should Be Involved in Engineering a Secure Vehicle by Design, (3) Engineering Vendor Teams Should Detect and Respond to Major Incidents within the Vehicle Lifecycle.

Research on Risk and Legal Regulation in Transnational Investment: Based on the comparison of current legislation in China and the United States

The enterprise transnational investment is thriving, and our economy is developing rapidly, but facing a shortage of talent, lack of technical development, management is not in place, investment risks, and so on many problems. From the perspective of national investment policies, developed countries have strengthened investment restrictions, and these measures are related to national security such as critical infrastructure and core technologies. Instead, developing countries continue to ease policies to promote investment. At the same time, tax-related investment policy measures taken around the world over the past decade show that tax holidays and reduced corporate income tax (CIT) are the main ways countries attract investors. Using the method of comparative analysis, this paper first analyzes three kinds of risks in transnational investment, namely capital security, environment, and tax issues, and then sorts out the regulations on transnational investment in the current legislation of China and the United States from the aspects of environment and tax, and finally puts forward suggestions to improve China's relevant legislation.

The hybrid pulse amplitude width modulation scheme: high efficiency technique for dimmable VLC systems

Abstract Visible-light communications (VLC) is an environmentally friendly optical wireless communication (OWC) technology that operates in the 400–800 THz frequency band. It is currently a critical technology for 5G and 6G wireless indoor applications due to its high data rates, low latency, and superior reliability. Intensity modulated direct detection (IM/DD) is widely used in VLC systems. A channel modulation scheme is designed using L-level pulse modulation techniques such as on–off keying (OOK), amplitude modulation (PAM), position modulation (PPM), and the width modulation (PWM). We introduce a novel hybrid modulation scheme n-M-PAWM based on both PAM and PWM schemes to improve throughput and significantly increase power and spectrum efficiency in VLC communication systems. The performance of our proposed hybrid n-M-PAWM scheme is studied in several aspects, including the number of transmitted bits per symbol, average BER, information rate ratio (IRR), PAPR, bandwidth, and power requirement. In an extremely noisy VLC system, the proposed 2-M-PAWM modulation outperforms both PPWM and PAPM modulation schemes due to its better BER performances. Additionally, we examine the performance of the VLC system in terms of spectral efficiency (SE) using our proposed PAWM-based dimming control system by varying the number of pulses transmitted during one symbol period. We demonstrate through simulation results that our PAWM-based dimming control system performs significantly better compared to the other pulse modulation schemes. All results of this research show that our proposed hybrid PAWM modulation scheme has high potential for VLC technology in indoor and noisy environments.

Controlling For X: Export Controls and Emerging Technology Competition

The prevailing view in Washington is that the United States and China are in a fierce competition for control of critical technologies of the future. The Biden Administration’s October 2022 semiconductor export control rules target China’s ability to make or obtain the most advanced semiconductors – the foundational components of advanced technologies. It is best to understand the United States semiconductor export controls as a tool being wielded by the US government in a fight for global technological dominance, particularly in the realm of AI. This strategy represents a substantial assertion of extraterritorial power by the United States, increases the risk of catastrophic conflict between the United States and China, and has the potential to backfire by accelerating China’s domestic semiconductor industry development. The United States should consider easing elements of the semiconductor export controls in exchange for diplomatic concessions, such as greater bilateral military communication, and focus more attention on increasing domestic production of advanced semiconductor technologies by fast tracking expert visas and permitting for new semiconductor fabrication facilities.

Remote teaching in the Mathematics degree course at the Federal University of Acre: perspectives, difficulties and future possibilities

This research aimed to identify potentialities and protect from remote moments between professors and students of the Mathematics Degree Course at the Federal University of Acre during the pandemic moment, as well as actions that can be worked on by the subjects for a “next” post-pandemic. As methodological resources and source of information, teachers and students were consulted through an intuitive command to identify difficulties/concerns and possibilities with respect to development actions in the period called Emergency Remote. This investigation was qualitative in nature, as I understand that I deepened the understanding of the social group in question and its trajectory does not require quantification. The information was processed in the light of the Philosophy of Mathematics Education, Critical Mathematics Education and Digital Technologies in Mathematics Education. The quality of the students' internet and the possibilities for evaluating students by teachers represented the greatest difficulties encountered during the pandemic. representaram as maiores dificuldades encontradas no momento pandêmico.

Integrated energy system scheduling considering the correlation of uncertainties

Under the circumstances of “double carbon”, it is imminent to promote the transition of energy. Integrated energy systems (IES) are one of the critical technologies for the energy transition. To achieve optimal operation of IES with uncertainty to reduce system operational cost and carbon emission. In this paper, we propose an optimal scheduling method considering the correlation between uncertainties with source-source, load-load, and source-load. Scenario generation with Wasserstein deep convolutional generative adversarial network-gradient penalty (WDCGAN-GP) captures the correlation between uncertainties. The laddered carbon transaction mechanism is introduced to control carbon emissions. The simulation analysis of the method is performed with the case of the park integrated energy system (PIES) to explore the impact of correlation on IES scheduling. The simulation results show that the carbon emission of laddered carbon transactions is reduced by 8.75% compared with the conventional, while the total cost is increased by 11.09%; on the basis, after considering the correlation, the carbon emission is reduced by 6.22%, and the total cost is decreased by 5.62%. Clearly, the correlation provides financial and environmental benefits to the system. This study provides theory instruction for IES scheduling under uncertainty, which with enormous potential for engineering applications.