High-end Equipment Research Articles

Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment.

The assessment of physiological signals such as the electroencephalography (EEG) has become a key point in the research area of emotion detection. This study compares the performance of two EEG devices, a low-cost brain-computer interface (BCI) (Emotiv EPOC+) and a high-end EEG (BrainVision), for the detection of four emotional conditions over 20 participants. For that purpose, signals were acquired with both devices under the same experimental procedure, and a comparison was made under three different scenarios, according to the number of channels selected and the sampling frequency of the signals analyzed. A total of 16 statistical, spectral and entropy features were extracted from the EEG recordings. A statistical analysis revealed a major number of statistically significant features for the high-end EEG than the BCI device under the three comparative scenarios. In addition, different machine learning algorithms were used for evaluating the classification performance of the features extracted from high-end EEG and low-cost BCI in each scenario. Artificial neural networks reported the best performance for both devices with an F[Formula: see text]-score of 75.08% for BCI and 98.78% for EEG. Although the professional EEG outcomes were higher than the low-cost BCI ones, both devices demonstrated a notable performance for the classification of the four emotional conditions.

Cooperative Network Embedding, Knowledge Network Structure and Technological Catch-Up of Latecomers: A Technical Standards Alliance Perspective

Taking the technical standards alliance (TSA) of strategic emerging industries as the sample, including China’s new energy vehicles, new-generation information technology, new materials, and high-end equipment manufacturing, the article empirically verified the impact of the external cooperation network and internal knowledge network of late-developing enterprises on their dual catch-up. The results showed that the appropriate centrality of the cooperative network promotes the exploitative catch-up and exploratory catch-up of late-developing enterprises. However, the excessive centrality blocked its exploratory catch-up. The structural hole of the cooperation network was conducive to the exploratory catch-up of late-developing enterprises, but not to their exploitative catch-up. The comprehensive cohesiveness of the knowledge network strengthened the positive impact of the centrality of the cooperation network on exploitative catch-up of late-developing enterprises, damaging the negative impact of the structural hole of the cooperation network on exploitative catch-up. The partial cohesiveness of the knowledge network positively adjusted the centrality of the cooperative network, the relationship between the structural hole and exploratory catch-up, and negatively adjusted the relationship between the centrality of the network and exploitative catch-up. By analyzing the differential impact of the dual network on two types of technology catch-up strategies of late-developing enterprises, the article deepened the theory of organizational duality. Meanwhile, the article contained innovation activities of late-developing enterprises in the TSA, which provided a new theoretical perspective and empirical basis for the combination of standardization of cooperation and innovation management theory.

Pothole recognition using DeepCNN with layer permutation scheme

Pavements need frequent maintenance and management, for which one needs detailed information related to the road condition. At present, much of the condition monitoring around the world is done manually in situ or using high-end equipment. This is quite impractical, especially on a national or global scale because it takes time and involves high cost and effort. Pothole recognition using deep-learning-inspired image classification is currently being researched. Convolutional neural networks (CNNs) have drastically enhanced the techniques of image classification. We discuss an improved model based on CNN for pothole recognition. It introduces a neural architecture, namely layer permutation scheme for CNN (LPS-CNN). There is always a trade-off between network complexity and processing time efficiency in neural network architectures. The specific importance of the proposed neural network is that it learns a sparse architecture of the receptive neurons, which turns out to be fruitful in attaining maximum efficiency (for pothole recognition) with a reasonable processing time. The practical applicability of the proposed model is tested on the publicly available DSH 2017 pothole dataset. The proposed LPS-CNN outperforms all other existing architectures for pothole recognition and achieves an exceptional average accuracy of 99.6% on the benchmark pothole dataset.

Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR.

Digital green innovation (DGI) is the core factor that affects the digitalization and decarbonization strategy of agricultural high-end equipment manufacturing (AHEM) system. Although AHEM enterprises actively cooperate with academic research institutes to develop agricultural high-end equipment, there are many obstacles in the process of DGI. Moreover, the integration of digital technology and green innovation from the perspective of partner matching for the AHEM system has not been fully introduced in current literature. Hence, this study aimed to (i) establish a suitable framework system for the AHEM system in general, (ii) quantify the selection of DGI by academic research institutions based on niche theory, and (iii) propose an extended niche field model combined with fuzzy VIKOR model. First, a theoretical framework consisting of three core elements of technology superposition, mutual benefit, and mutual trust, and technological complementarity was constructed based on niche intensity and niche overlap degree. DGI ability superposition of technology, mutual trust, and technical complementarity are beneficial for transferring DGI knowledge from academic research institutes to the AHEM industry. Second, triangle fuzzy number and prospect theory combined with the VIKOR method were introduced into the field theory to construct the complementary field model of DGI resources. The niche field model has been successfully applied to practical cases to illustrate how the model can be implemented to solve the problem of DGI partner selection. Third, the results of a case study show that the criteria framework and the niche field model can be applied to real-world partner selection for AHEM enterprises. This study not only puts forward the standard framework of niche fitness evaluation based on niche theory but also establishes the niche domain model of innovation partner selection management based on niche theory. The standard framework and novel niche field model can help enterprises to carry out digital green innovation in the development of high-end agricultural equipment. The study has the following theoretical and practical implications: (i) constructing a criteria framework based on niche theory; (ii) developing a novel niche field model for DGI partner selection of AHEM enterprises; and (iii) assisting AHEM enterprises to perform DGI practice.

Intelligent machinery health prognostics under variable operation conditions with limited and variable-length data

High accuracy health prognostics are significant to machinery intelligent operation and maintenance. Current data-driven prognostics achieve great success that benefits from amply learning samples. In fact, data scarcity challenge widely exists in machinery prognostics and health management, especially for high-end equipment. This study aims to solve this dilemma and proposes a novel meta learning algorithm reconstructed by classic variable-length prediction mode and attention mechanism, namely meta attention recurrent neural network (MARNN). Specifically, we first develop the encoder-decoder with attention mechanism (EDA) cell to perform episodic learning for the subtask-level upgrade. Then multiple subtasks with EDA as prediction models are aggregated to accomplish meta-level upgrade, thus mining the general degradation knowledge from historical datasets. Finally, cross-domain prognostics tasks can be easily realized through fine-tuning tricks, and three rotating machinery run-to-failed experiments are conducted to prove the generalizations of MARNN, which can obtain desired results even when the on-site adaptation data is reduced to one-twentieth.

The Employability of Graduates of National Characteristic Discipline Programs of Study in China: Evidence from Employers

Prior studies have examined the historical evolution, multiple stakes, measurements and operation models of graduate employability, and the researches on graduate employability have gradually shifted to the perspective of employers with wider labor market uncertainty and higher education massification. However, there is still a gap in research on the demand for graduate employability by employers in national high-end equipment manufacturing that work closely with higher education in scientific research. Namely, it remains unclear what really matters in the processes of employers’ recruitment decisions in national high-end equipment manufacturing. Drawing on Yorke’s definition and CareerEDGE model, this study defines graduate employability as a set of achievements—skills, understandings and personal attributes—that makes graduates more likely to gain employment in national high-end equipment manufacturing, including “emotional intelligence”, “knowledge and skills”, “generic skills”, “work experience”, “character and personality”. Owing to the importance and arduousness of national high-end equipment manufacturing historical mission and main tasks, we argue that employers pay more attention to graduate employability in the recruitment process. Empirically examining based on 831 questionnaires from employers of national high-end equipment manufacturing in China, we show that employers prefer graduates with higher levels of “cooperative innovation ability”, “knowledge and skills”, “stress management and adaptation” within Chinese national characteristic discipline programs. Particularly, although employers in national high-end equipment manufacturing have always emphasized employees’ loyalty and dedication, “character and personality” of a graduate does not have a direct effect on employer hiring preference, but instead the effect of cooperative innovation ability and knowledge and skills are fully moderated by character and personality.

Empirical Decomposition and Forecast of Carbon Neutrality for High-End Equipment Manufacturing Industries

The Chinese government focuses on the high-end equipment manufacturing industry to achieve a target of carbon neutrality. This study takes China’s Bohai Rim as a case study. First, the Tapio decoupling model was used to analyze the carbon emission status of the high-end equipment manufacturing industry in the Bohai Rim. Second, LMDI was used to determine the main factors of carbon emission. Similarly, the Monte Carlo simulation predicted the time of carbon neutrality. The results found that the relationship between carbon emission and the development of the high-end equipment manufacturing industry is that of strong decoupling, but there is still a risk of “recoupling.” The scale effect is the primary driving force for carbon emission reduction in the equipment manufacturing industry, followed by a structural effect and a carbon emission intensity effect. In the baseline scenario, low-carbon scenario, and technological breakthrough scenario, carbon neutrality will be achieved before 2060. The results of the study suggest that China should improve energy utilization efficiency and encourage green innovation.

A review on the mechanical property evaluation and optimization design of fabric rubber composite structure

Fabric rubber composite structure is a composite structure with rubber as the matrix and fiber as the reinforcement, which is widely used in high-end equipment due to its excellent mechanical property and sealing performance. The mechanical property, multiscale analysis techniques and optimization design methods of fabric rubber composite structure in a complex service environment are the focuses of current research and application fields. This article will systematically review the research progresses in the mechanical property evaluation and optimization design of fabric rubber composite structure in recent years from the aspects of theory, experiment, simulation, etc. This will deepen the understanding of the design concepts of fabric rubber composite structure. It has important scientific guiding significance for the innovative design of fabric rubber composite structure.

A self-sensing and self-actuating metamaterial sandwich structure for the low-frequency vibration mitigation and isolation

Sandwich structures (SSs) are widely deployed in engineering applications, such as for the low-frequency vibration or acoustic mitigation and isolation. However, it is challenging to enhance their low-frequency vibration mitigation and isolation performance while maintaining their multi-functionalities, such as lightweight, high bending stiffness, and high impact absorption ability, etc. To achieve this objective, local resonators are introduced into a hollow-tube core lattice sandwich structure (HLSS) in this paper, which is referred to as the metamaterial sandwich structure (MMSS-1). The proposed MMSS-1 is composed of periodically distributed thin-walled hollow tubes with attached resonators as the core, and two thin cover-plates or face-sheets. The band structures and the corresponding eigenmodes of the unit-cell of the MMSS show that both the Bragg scattering and the local resonance band-gaps coexist in the proposed MMSS-1. The influences of the key geometrical and material parameters of the MMSS-1 on the band-gaps are analyzed in details. Then, the piezoelectric patches are attached to the top and bottom face-plate surfaces of the MMSS-1 and resonators to form the MMSS-2, and they are shunted via an active feedback control to build the MMSS-3. The effects of the displacement feedback control gains on the band-gaps of the MMSS-3 are studied. Based on the active displacement feedback control and differential evolution (DE) optimization algorithm, a self-sensing and self-actuating strategy is developed to lend the self-adaptive capability to the MMSS-3, which is referred to as the self-adaptive MMSS. The vibration or flexural wave propagation in the finite-sized self-adaptive MMSS is numerically simulated to demonstrate its outstanding ability for low-frequency vibration mitigation and isolation. It shows that the proposed self-adaptive MMSS can evolve different control parameters automatically to widen and merge Bragg scattering band-gaps and local resonance band-gaps without human intervention. In addition, the vibration attenuation inside the band-gaps is also enhanced. The proposed self-sensing and self-actuating strategy can improve the vibration mitigation and isolation performance of the HLSSs significantly in the low-frequency range. The proposed self-adaptive MMSS may pave a novel and feasible way for the design and applications of the HLSSs for the low-frequency vibration mitigation and isolation in civil engineering, mechanical engineering, and aerospace engineering etc. Moreover, the proposed self-adaptive strategy via self-sensing and self-actuating can be also applied to other types of the SSs for improving their vibration mitigation and isolation performance, and for the NVH (noise, vibration and harshness) of vehicles, high-end equipment and high-precision instruments.

Influence of Surface Texture on Sealing Performance of PTFE Materials

Due to the hydrodynamic pressure effect, the bearing capacity of the oil film on the surface of a textured friction pair is greater than that of a smooth surface. In this paper, the effects of PTFE surface texture parameters (shape, depth, width, and area ratio) on the oil film bearing capacity and leakage of the sealing system under lubrication are studied using the FLURNT simulation. It is found in this present study that greater texture depths do not necessarily lead to better sealing performance. When the texture depth exceeds a certain level, a reverse flow occurs at the bottom of the texture, thereby weakening the hydrodynamic pressure effect. An optimal texture depth of 5–10 μm maximizes the oil film bearing capacity. Within a certain range, the oil film bearing capacity increases along with texture widths. In addition, leakage of the rectangular texture rises significantly with growing texture widths. Larger texture area ratios result in higher leakage, but the bearing capacity first rises and then falls with an increase in the area ratio, with a maximum value of 70–80%. Considering the influence of texture parameters on oil film bearing capacity and leakage, selecting the most appropriate texture parameters for surface texture treatment optimizes the performance of the sealing system. The findings of this paper provide a theoretical basis for improving the sealing performance of high-end aviation equipment using texture treatment, thereby enabling the application of surface texture technology to improve the tribological properties of materials.

In-situ γ-ray analysis of ground surface radioactivity using portable HPGe γ spectrometer.

As essential high-end equipment for nuclear emergency monitoring, the portable HPGe γ spectrometer currently lacks supporting in-situ measurement methods, limiting its role and value in emergency missions. For this practical problem, this paper studies the measurement of ground surface radioactivity by portable HPGe γ spectrometer in nuclear emergency monitoring in view of the particularity of nuclear emergency source items. Firstly, the detection efficiency of point sources at different horizontal distances when the spectrometer is installed at the height of 1 m from the center of the detector to the ground is calculated. Secondly, the concept of effective contribution distance is defined and analyzed. Thirdly, the point source detection efficiency is obtained using the numerical integration method of calculation. Integrate to calculate the detection efficiency of the surface source, and then calculate the radioactive surface activity of the surface. Finally, the effectiveness of the method is verified through experiments.

Vinyl as Fine Wine: The Role of Expectation on the Perception of Music Format.

While vinyl, compact discs, and even eight-track tapes were traditionally promoted to consumers as producing superior sound, the introduction of compressed digital music, such as mp3s, was markedly different. Initially, one of the primary selling features of digital music was convenience and portability rather than sound quality. Recently, vinyl music sales have experienced a substantial resurgence. Waveforms from vinyl represent recorded music more accurately than compressed digital formats and have the potential to produce better sound. Even so, most music listeners do not reliably listen to music on audiophile quality high-end equipment. For this reason, we believe one aspect of vinyl sales is the expectation that vinyl quality is superior. In this study, we sought to isolate the contribution of expectation to perceived sound quality. Participants were asked to listen to a selection of music on either vinyl or mp3. Some participants were told that they were listening to vinyl when the musical selection was an mp3, while others were told they were listening to an mp3 while actually listening to vinyl. A multivariate analysis through a Canonical Correlation Analysis established that expectation of music format quality drove post-listening evaluations.

Dynamic resource allocation and collaborative scheduling in R&D and manufacturing processes of high-end equipment with budget constraint

Dynamic resource allocation and collaborative scheduling in R&D and manufacturing processes of high-end equipment with budget constraint

Passive wireless strain and crack sensing using a RFID-based patch antenna

As an important parameter of structural health monitoring, strain can be an important indicator for stress concentration and crack development. And stress/crack can effectively evaluate the health status of the structure. Micro-strip antenna strain sensing technology, as a passive wireless detection technology, is of great significance to the health monitoring of high-end equipment bearing structures. In this work, the UHF (Ultra High Frequency) RFID (Radio Frequency Identification) technology is combined with the micro-strip antenna technology. And the micro-strip antenna is used as the tag antenna to receive the energy emitted by the RFID reader and activate the tag chip of the RFID. Then the signal modulation technology is used to eliminate the noise interference in the environment, and the resonant frequency of the antenna changes with the strain/crack information is returned to the reader to realize the passive wireless strain and crack detection. Through the experiment, it proves that under different strain/crack states, the resonant frequency of the RFID-based antenna sensor decreases with the increase of strain or the increase of the depth of the crack.

High-end equipment: An improved two-sided based S&M matching and a novel Pareto refining method considering consistency

In order to avoid resources waste and monopoly in high-end equipment manufacturing, the concept of a cloud platform was proposed, in which suppliers and manufacturers can match each other in two-sided. As a result of the increasing information complexity and the fuzziness of human cognition, matching evaluation values may contain some hesitancy and ambiguity. To simulate this situation, we consequently propose an improved two-sided-based S&M matching model and a novel Pareto refining method for a high-end equipment cloud manufacturing platform under a hesitant fuzzy environment. Firstly, the traditional maximum deviation method for calculating attribute weights is improved to adapt to multiple agents in S&M matching. Sequentially, the hesitant fuzzy set (HFS) is applied to describe the fuzziness of decision agents. Simultaneously, we innovatively introduce the score and deviation of HFS to comprehensive satisfaction degree calculating as objective functions. Subsequently, considering the matching consistency, we declare a novel Pareto refining method based on an interval-valued satisfaction matrix to deal with Pareto solutions in the two-sided matching multiple objective model. Finally, an illustrative case is employed to prove the practicability and usability of the two-sided-based S&M matching model in the high-end equipment cloud manufacturing platform. It reveals that this model can not only obtain multiple matching pairs with maximal satisfaction but also can select the most consistent S&M portfolio.

Sulfonated Block Ionomers Enable Transparent, Fire-Resistant, Tough yet Strong Polycarbonate

• Multifunctional ionomer (sSEBS-Ce) is rationally designed and synthesized. • 1.5 wt% of sSEBS-Ce obviously increases the fire resistance and toughness of PC. • The tensile strength and transparency of PC/sSEBS-Ce are well-preserved. • Structure-composition-property correlation of sSEBS-Ce in PC is revealed. Polycarbonate (PC) features high transparency and balanced mechanical properties, and thus is being growingly used for producing many high-end products, e.g. , construction facades, sensors and 5G equipment. For these applications, PC is required to combine satisfactory fire retardancy and great toughness while retain its mechanical strength and optical transparency. However, existing either fire retardants or toughening agents fail to enable PC to achieve such a required performance portfolio due to their improper molecular designs. To overcome this challenge, we, herein, rationally design a series of sulfonated ionomeric fire retardants (sSEBS-M, M = Na + , Zn 2+ , Ce 3+ ) by sulfonating and neutralizing styrene-ethylene-butylene-styrene (SEBS). The sSEBS-M can be well-dispersed within the PC matrix with phase domain sizes less than 500 nm. Chemical structures of sSEBS-M and their dispersion within the polymer matrix strongly correlate to their comprehensive performances in PC. Among three sSEBS-M ionomers, sSEBS-Ce endows PC with better comprehensive performances. With 1.5 wt% of sSEBS-Ce, the final PC achieves a high limiting oxygen index of 33.5% and a desired UL-94 V-0 rating, in addition to a 53% reduction in peak heat release rate and a comparable transparency to virgin PC. Moreover, its impact toughness and ductility are enhanced by 40% and 116% with tensile strength well-preserved. The integrated performance portfolios are superior to previous counterparts. This work offers a novel strategy for the design of multifunctional ionomer-based fire retardants for creating high-performance PC and reveals its structure-composition-property relationship in PC, which will enable PC to realize its practical applications in above-mentioned industries.

Buckling enhancement of tubular metamaterial with axial zero thermal expansion by integrating two adjustment mechanisms

Artificially designed mechanical metamaterials with desired property of zero thermal expansion (ZTE) have already made great progress motived by the urgent needs of high-end equipment and instruments served in large fluctuating temperature environment. Various thermal expansion adjustment mechanisms are developed to achieve controllable thermal deformation. However, only designing ZTE is not normally sufficient, but must be combined with enough mechanical performances for carrying mechanical loads. Hence in this study, a method of bucking enhancement for designing tubular metamaterials with axial ZTE is firstly proposed by integrating two existing adjustment mechanisms. Compared with the previous design under the single Poisson contraction mechanism, the present axial ZTE property is mainly achieved through thermally bending-adjustment mechanism, and therefore avoid the unfeasibility of requiring too large thermal expansion coefficient difference for constituent materials. Meanwhile, the significant buckling capacity loss caused by the introduced initial curvature used for triggering thermally bending-adjustment mechanism is prominently improved by taking the advantage of Poisson contraction mechanism. The results obtained from detailed numerical simulations verify the design targets of simultaneous axial ZTE and buckling enhancement. The proposed design strategy of mechanism combination is also proved effective to enhance the buckling capacity of present dual-mechanism metamaterial without obvious increase of structural mass.

Point-of-care ultrasound for acute abdomen: 5W1H(Translated version).

In this paper, point-of-care ultrasound (POCUS) for the initial diagnosis and the management of acute abdomen is discussed. POCUS is supposed to be executed mainly by doctors other than ultrasound specialists anytime and anywhere such as in the emergency room or the intensive care unit. Although it seems rather difficult to cover the wide spectrum of organs as well as diseases causing acute abdominal pain, the author advocates a "six approach" for the diagnosis and triage of acute abdomen, which consists of scanning at eight points in less than 5min. With this method, the attending doctor can diagnose most of the diseases frequently encountered in patients with acute abdomen, which can help patients avoid unnecessary examinations or admissions. However, users of POCUS should be aware of its limitations, especially when they are using pocket-sized ultrasound equipment. Therefore, users should be careful when ruling out a disease even when they cannot find any pathological findings, and consider the need for further examinations such as US done by specialists with high-end equipment or CT. Since there has been no standard curriculum in Japan for POCUS training that should deal with basic physics and techniques for US, normal abdominal anatomy, typical pathological US findings, and interventional US, the establishment of a learning program for doctors and training of experts as instructors of POCUS are needed.

Enhancing explosive weldability of thick precipitate-hardened alloys

Explosive welding of high-strength precipitate-hardened alloys in large thickness was very challenging but was increasingly desired with the continuous development of high-end equipment. An innovative strategy, which was previously proposed to achieve joining at the solid-solutioned state at first and improve the composite properties by later thermal treatment, was validated here using a Cu-Ni-Si-Cr alloy as a model material. Under the same welding condition, the composite welded at the solid-solutioned state exhibited a larger wave amplitude than that joined at the peak-aged state, an evident proof of better weldability. Further numerical analysis indicated that the enhanced weldability of the solid-solutioned alloy was closely related with its lower deformation resistance. The composite joined at the solid-solutioned state achieved a slightly lower peak hardness and yield strength than the counterpart for the thermally weakened deformation hardening effects and the more interfacial defects, while the electrical conductivity of them was comparable.

Strategic emerging industry layout based on analytic hierarchy process and fuzzy comprehensive evaluation: A case study of Sichuan province.

In the 12th Five-Year Plan period, Sichuan province has selected strategic emerging industries such as new-generation information technology, new energy, high-end equipment manufacturing, new materials, biology, energy conservation and environmental protection. However, its strategic emerging industries are still in the stage of cultivation and development, and there are some problems such as low industrial agglomeration and obvious industrial restructuring. In essence, this is because the focus of the development of strategic emerging industries is not clear enough, and the location advantages are not fully utilized to select the most suitable strategic emerging industries for the development of the region. This paper constructs a regional strategic emerging industries selection decision model, applies ARCGIS to study the spatial distribution of strategic emerging industries in Sichuan province, and uses fuzzy comprehensive evaluation method (FCEM) and analytic hierarchy process (AHP) to solve the priority of developing strategic emerging industries. Conclusions: Firstly, Sichuan province should give priority to the development of new generation information technology industry and new energy vehicle industry, then high-end equipment manufacturing industry, energy-saving and environmental protection industry and new energy industry, and finally biological industry and new material industry. The larger the coefficient of influence is, the higher the comprehensive score is, while the larger the coefficient of sensitivity is, the lower the comprehensive score is. Secondly, the number of enterprises in the new-generation information technology industry and the new energy vehicle industry is still not dominant in Sichuan province. Finally, the study found that the current development of strategic emerging industries in Sichuan province is extremely unbalanced in various regions, and the phenomenon of competition and reconstruction is obvious.