Hasil untuk "Automation"

RAO Qingyang, YANG Qiongbo, CUI Dongwen

To improve over-fitting of data processing, weak time series modeling, and difficult selection of hyperparameters in the Kolmogorov-Arnold network (Kan), a groundwater level prediction model based on wavelet packet transform (WPT) secondary decomposition and Chinese Pangolin optimizer (CPO) algorithm was proposed to optimize KAN hyperparameters, and WPT-CPO-Transformer, WPT-CPO-LSTM, WPT-CPO-gated circulation unit (GRU), WPT-CPO-least squares support vector machine (LSSVM), WPT-CPO-extreme gradient ascent machine (XGBoost), WPT-CPO-MLP, and WPT-KAN were constructed. These seven kinds of comparative analysis models were verified by the daily average groundwater level time series prediction examples of Xicheng, Wenlan, Lin'an, and Caoba stations in Yunnan Province. Firstly, the WPT secondary decomposition technology was used to decompose the groundwater level time series data and divide the training set and the verification set. Then, the CPO was used to optimize the hyperparameters of KAN to overcome the tedious and inefficient manual debugging and avoid local optimization. Finally, the WPT-CPO-KAN model was established by using the optimal hyperparameters to train, predict, and reconstruct the decomposed components of the groundwater level time series. The results show that: (1) compared with that of the WPT-CPO-Transformer, WPT-CPO-LSTM, WPT-CPO-GRU, WPT-CPO-XGBoost, WPT-CPO-LSSVM, WPT-CPO-MLP, and WPT-KAN models, the prediction accuracy of the WPT-CPO-KAN model is improved by 15.6%, 37.4%, 26.5%, 36.4%, 18.6%, 7.2%, and 26.7%, respectively (MAPE index), which has a smaller prediction error and better universality. (2) Under the same WPT secondary decomposition and CPO, KAN can better capture the complex nonlinear space and time dependence in groundwater level time series data and is more suitable for the distribution of groundwater level time series data. Its performance is better than that of the transformer, LSTM, GRU, XGBoost models, traditional LSSVM, and MLP network. (3) The prediction error of the WPT-CPO-KAN model increases with the increase in the prediction step. Within three days, the prediction accuracy of the WPT-CPO-KAN model is higher. (4) The reasonable selection of hyperparameters is of great significance to improve the performance of the KAN model. By using CPO to optimize KAN hyperparameters, the performance of KAN and the level of prediction automation are significantly improved. The optimization method can provide a reference for improving the performance of KAN. (5) KAN can reveal the variation characteristics of groundwater level time series data with fewer parameters, thus enhancing the interpretability of the WPT-CPO-KAN model.

Chao Zhang, Ya-Qin Qiu, Zi-Ao Li

Within the field of permanent magnet synchronous motor sensorless speed control systems, we present a novel scheme with a Multi-dimensional Taylor Network (MTN)-based nonlinear observer as the core, supplemented by two auxiliary MTN modules to realize closed-loop control: (1) MTN Model Identifier: Provides real-time PMSM nonlinear dynamic feedback for the observer; (2) MTN Adaptive Inverse Controller: Compensates for load disturbances using the observer’s estimated states. The study focuses on optimizing the MTN observer to address key limitations of existing methods (high computational complexity, lack of stability guarantees, and low estimation accuracy). Compared with the neural network observer, this MTN-based scheme stands out due to its straightforward structure and significantly reduced approximately 40% computational complexity. Specifically, the intricate calculations and high resource consumption typically associated with neural network observers are circumvented. Subsequently, by leveraging Lyapunov theory, an adaptive learning rule for the MTN weights is meticulously devised, which seamlessly bridges the theoretical proof of the nonlinear observer’s stability. Simulation results demonstrate that the proposed MTN observer achieves rapid convergence of speed and position estimation errors (with steady-state errors within ±0.5% of the rated speed and ±0.02 rad for rotor position) after a transient period of less than 0.2 s. Even when stator resistance is increased by tenfold to simulate parameter variations, the observer maintains high estimation accuracy, with speed and position errors increasing by no more than 1.2% and 0.05 rad, respectively, showcasing strong robustness. These results collectively confirm the efficacy and practical value of the proposed scheme in PMSM sensorless speed control.

Dino Petrosa, Pamela Haverkamp, Jana Gerta Backes et al.

Abstract The construction sector significantly contributes to environmental issues and often relies on Life Cycle Assessment (LCA) for the quantification and optimization of its environmental impacts. One of the most time- and labour-intensive tasks in LCA is matching real elements (e.g., construction elements and materials) to suitable environmental datasets to get an idea of the element’s sustainability performance (emissions). In this regard, this study presents an open-access software tool that leverages artificial intelligence (AI) to support the matching process between construction elements in Building Information Modelling (BIM) with corresponding environmental datasets in a semi-automatic manner. Developed in Python and using the GPT-4o mini model from OpenAI for its matching mechanism, the tool demonstrates how AI-driven digital innovation can improve efficiency, reduce manual effort, and enhance early-stage environmental assessment in construction planning, while integrating sustainability data into BIM workflows. Through a series of use cases, the software’s ability to address key challenges in the integration of BIM and LCA tools is demonstrated, showcasing a high degree of automation and interoperability. Moreover, the accessible design of the tool allows use without extensive technical knowledge. The conducted validation tests confirmed the tool’s potential for accurate LCA matching, highlighting opportunities for AI to enhance sustainability workflows while offering BIM experts a better understanding of the challenges in sustainability assessment.

Ning Li, Zhiyu Xiang, Liang Feng et al.

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH). This approach aims to conserve energy while maintaining acceptable network latency levels. The wakeup threshold restricts the frequency of state switching for the CH, thereby reducing energy consumption. Meanwhile, the dynamic access probability regulates the influx of packets to mitigate system congestion based on current network conditions. Furthermore, to accommodate the network’s varied transmission demands, packets generated by sensor nodes (SNs) are categorized into two types according to their sensitivity to latency. A discrete−time queueing model with preemptive priority is then established to evaluate the performance of different packets and the CH. Numerical results show how different parameters affect network performance and demonstrate that the proposed mixed packet forwarding mechanism can effectively manage the trade−off between latency and energy consumption, outperforming the traditional mechanism within a specific range of parameters.

V. Ya. Tsvetkov

The development of society is accompanied by an increase in the complexity of management objects and management mechanisms. To counteract the growth of complexity, new management models and methods should be introduced. New methods include semasiological management which uses a model approach and induction principle. It borrows the ideas of semasiology from linguistics and forms management decisions on the basis of application of information management units. Despite the fact that this complicates the preliminary process of preparing for management, it also gives an advantage in the comparability of different management decisions and technologies. Semasiological management allows, when reconfiguring management, not to create management models anew, but to modernise them by replacing management information units or forming new combinations of these units. Semasiological management is related to onomasiological information modeling and requires its use. In addition, it can be used in automated management, smart management, and digital twin management. Semasiological management requires special organisation and specific training, such as a special management language. The research proposes a variant of semasiological management which is based on the application of the theory of information units.

Iván Claudio Suazo Galdames

Introduction: The introduction of artificial intelligence-driven applications is revolutionizing dentomaxillofacial imaging. Objectives: To describe the current status of artificial intelligence applications in dentomaxillofacial diagnostics; to assess their impact; and to identify future directions for research and implementation. Methods: A narrative review was performed, using systematic searches in databases such as PubMed, Google Scholar, IEEE Xplore, among others; the study focused on articles published from 2010 to the present. Researches applying artificial intelligence technologies in dentomaxillofacial diagnosis were included; their quality and relevance were evaluated using the established tools. Results: Artificial intelligence, especially deep learning, has shown significant improvements in image segmentation, disease detection and treatment planning in dentomaxillofacial imaging. Artificial intelligence techniques have enabled automation of image analysis tasks, improved efficiency and diagnostic accuracy. Conclusions: Artificial intelligence has significant potential to revolutionize dentomaxillofacial imaging, as it offers improvements in diagnostic accuracy, efficiency in image interpretation, and treatment planning. Further research is needed to overcome technical, ethical and privacy challenges and to validate the clinical applicability of these technologies.

Haihua Qin, Jiafang Pan, Jian Li et al.

Intelligent fault diagnosis encounters the challenges of varying working conditions and sample class imbalance individually, but very few approaches address both challenges simultaneously. This article proposes an improvement network model named ICDAN-F, which can deal with fault diagnosis scenarios with class imbalance and working condition variations in an integrated way. First, Focal Loss, which was originally designed for target detection, is introduced to alleviate the sample class imbalance problem of fault diagnosis and emphasize the key features. Second, the domain discriminator is improved by the default ReLU activation function being replaced with Tanh so that useful negative value information can help extract transferable fault features. Extensive transfer experiments dealing with varying working conditions are conducted on two bearing fault datasets with the effect of class imbalance. The results show that the fault diagnosis performance of ICDAN-F outperforms several other widely used domain adaptation methods, achieving 99.76% and 96.76% fault diagnosis accuracies in Case 1 and Case 2, respectively, which predicts that ICDAN-F can handle both challenges in a cohesive manner.

Francesco Toscano, Costanza Fiorentino, Nicola Capece et al.

Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management that utilizes advanced technologies, such as sensor data analysis and automation, to optimize crop productivity, enhance farm income, and minimize environmental impacts. DPA encompasses various agricultural domains, including pest control, pest management, fertilization, irrigation management, sowing, transplanting, crop health monitoring, yield forecasting, harvesting, and post-harvest stages. Among the enabling technologies for DPA, Unmanned Aerial Vehicles (UAVs) have gained significant attention and market growth. The advancements in control systems, robotics, electronics, and artificial intelligence have led to the development of sophisticated agricultural drones. UAVs offer advantages such as versatility, quick and accurate remote sensing capabilities, and high-quality imaging at affordable prices. Furthermore, the miniaturization of sensors and advancements in nanotechnology enable UAVs to perform multiple operations simultaneously without compromising flight autonomy. However, various variables, including aircraft mass, payload capacity, size, battery characteristics, flight autonomy, cost, and environmental conditions, impact the performance and applicability of UAV systems in agriculture. The economic considerations involve the purchase of drones, equipment, and the expertise of trained pilots for flight management and data processing. Payload capacity, flight range, and financial factors influence agriculture’s choice and implementation of UAVs. The research and patent trends show the growing interest in UAVs for agricultural applications. This paper provides a general review of UAV types, construction architectures, and their diverse applications in agriculture until 2022.

Zhongxuan Liu, Jiayou Shi, Jingyi Xu

Scientific scandals are catalysts for the evolution process of legal governance. The 2018 CRISPR-babies Incident has essentially triggered China's legal reforms of ethics governance in science and technology. This paper explores the institutional deficiency that led to such a scandal, analyzes its long-term implications for legal governance, and presents China's recent legal progress in response to such an issue. The rapid legislative response to the CRISPR-babies Incident is a double-edged sword, while promoting the improvement of the legal system, it can also cause issues like fragmentation of governance, contradictory rules, and conflict of interest. China should integrate departmental norms and upgrade its level of effectiveness. Strengthening legislation is the implementation path, and improving ethical review, supervision and scientific research integrity systems are the crucial means. In addition, it is necessary to bring the coordinating function of the Central Science and Technology Commission into full play and pay more attention to public engagement and international cooperation.

Ye Yuan, Yongtong Zhu, Jiaqi Wang et al.

IntroductionSpiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-dependent plasticity (STDP), structural plasticity, and homeostatic plasticity, making network connection patterns and weights to change continuously during the lifecycle. However, it is unclear how these plasticities interact to shape neural networks and affect neural signal processing.MethodHere, we propose a reward-modulated self-organization recurrent network with structural plasticity (RSRN-SP) to investigate this issue. Specifically, RSRN-SP uses spikes to encode information, and incorporate multiple plasticities including reward-modulated spike timing-dependent plasticity (R-STDP), homeostatic plasticity, and structural plasticity. On the one hand, combined with homeostatic plasticity, R-STDP is presented to guide the updating of synaptic weights. On the other hand, structural plasticity is utilized to simulate the growth and pruning of synaptic connections.Results and discussionExtensive experiments for sequential learning tasks are conducted to demonstrate the representational ability of the RSRN-SP, including counting task, motion prediction, and motion generation. Furthermore, the simulations also indicate that the characteristics arose from the RSRN-SP are consistent with biological observations.

Xiaojiao Chen, Xiuqing Zhang, Mi Dong et al.

The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time series of wind power, it is present a two-stage modeling strategy, in which a deep neural network combines spatiotemporal correlation to simultaneously predict the power of multiple wind turbines. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). Herein, the LSTM captures the temporal dependence of the historical power sequence, while the CNN extracts the spatial features among the data, thereby achieving the power prediction for multiple wind turbines. The proposed approach is validated by using the wind power data from an offshore wind farm in China, and the results in comparison with other approaches shows the high prediction preciseness achieved by the proposed approach.

Quan Xu, Jun Chen, Linbo Luo et al.

The goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural- looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the images. Moreover, when an object in 3-D is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multiregion guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2-D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases.

Shih-Jeh Wu, Pei-Chieh Chin, Hawking Liu

The measurements of acoustic properties of three brittle materials i.e., ITO (alkaline earth boro-aluminosilicate) glass, bulk metallic glass (BMG) and nickel-based superalloy (CM247LC) are conducted in this work to obtain various properties. The elastic moduli of materials are derived from the results by simple acoustic speed-elasticity relationship and compared with the data obtained with nanoindentation. The difference between the Young’s modulus of ITO glass by ultrasonic and nanoindentation is 0.83%, a perfect match within range error. As for BMG, the difference (Young’s modulus) is 23.59%, and 5.11% for the CM247LC superalloys. The pulse-echo method proves to be reliable for homogeneous amorphous glass, however, the elastic moduli of metallic glass and CM247LC superalloy by ultrasonic are quite different from those by nanoindentation. The difference is large enough to cover the maximal error associated with the nanoindentation method. The relationship of acoustic speed and elastic constants must be reviewed in dealing with compound materials.

А. К. Сандлер, О. В. Дрозд

Розглянуті відомі конструкції роликових опор стрічкових транспортерів, як елементу вантажної системи судна. Визначені недоліки та шляхи вдосконалення. Запропоновано нове схемотехнічне рішення роликової опори.

Xu Jun, Li Qunqun, Wang Yuehui et al.

An indoor moving target positioning system using UWB(Ultra-wideband) technology was designed. Through the base station installed in a specific location in the room and the tag carried by the mobile object, the improved standard TDOA(Time Difference of Arrival) positioning algorithm obtains the real-time location information of the moving target and completes the accurate positioning of the indoor moving target. Qt was used to develop a PC application software that can display the position and movement trajectory of the positioning target in real time. Finally, an experimental test platform was set up to test the system and the obtained location information was compared with the actual data. The result shows that the overall system communication is stable and meets the positioning requirements.

Tao Wu, Zhenghong Deng, Qingyue Gu et al.

We explore the estimation of a two-dimensional (2D) nonsymmetric coherently distributed (CD) source using L-shaped arrays. Compared with a symmetric source, the modeling and estimation of a nonsymmetric source are more practical. A nonsymmetric CD source is established through modeling the deterministic angular signal distribution function as a summation of Gaussian probability density functions. Parameter estimation of the nonsymmetric distributed source is proposed under an expectation maximization (EM) framework. The proposed EM iterative calculation contains three steps in each cycle. Firstly, the nominal azimuth angles and nominal elevation angles of Gaussian components in the nonsymmetric source are obtained from the relationship of rotational invariance matrices. Then, angular spreads can be solved through one-dimensional (1D) searching based on nominal angles. Finally, the powers of Gaussian components are obtained by solving least-squares estimators. Simulations are conducted to verify the effectiveness of the nonsymmetric CD model and estimation technique.

Elif Kalayci

It is widely acknowledged that R&D based startups play a significant role in the economic growth of many countries. However, founding such an enterprise is a risky endeavor, one that requires a balance between the technological search process and business capabilities. Most of the time these varied skills are found among several different people. The task becomes more difficult for recent engineering school graduates who are neither scientists nor business people. Therefore, it is critical for these new techno-entrepreneurs to conscientiously work on building relationships with stakeholders through whom they might access scientific knowledge on one hand and commercial knowledge on the other. The paper explores the process of building relationships with stakeholders based on evidence from Turkish companies. It begins with a review of literature, presenting the different theories concerning relationships with stakeholders as far as entrepreneurship is concerned. Then, it presents the methodology, classification and analysis of in-depth interviews with the founders of R&D-based startups, which help justify the use of a qualitative approach. The case profiles are considered with a focus on the following issues: the counterbalancing of stakeholder power, learning by the entrepreneur as a by-product of interactions with stakeholders, and the earning of a reputation through ethical and passionate business practices. Building upon these preliminary findings, the author draws three main propositions that could be the subject of further research. The main finding of this paper is that there are two opposing forces affecting the development of any company — problem and supporter stakeholders. At that, a stakeholder who was once a supporter could turn into a challenger or vice versa. The entrepreneur could benefit from the counterbalancing effect of these forces. Two major stakeholder groups emerged at the initial stage of the business: the family members and the state’s grant-handling officers. Then, the ethical and passionate conduct of business by these startups could become a factor drawing third parties in to become stakeholders of these startups. The nature and impact of these relationships should be researched further. Such an analysis allows one to understand how R&D-based startups are established and what kind of problems they face when turning (hopefully) into large corporations. On such a basis, this could help governments develop more suitable support programs that would benefit and expand the opportunities available to the founders of new R&D-based firms.

Zhi-Bin Wang, Bo-Wen Li, Qiu-Yue Nie et al.

Propagation characteristics of electromagnetic (EM) waves in partially ionized plasma slabs are studied in this paper. Such features are significant to applications in plasma antennas, blackout of re-entry flying vehicles, wave energy injection to plasmas, and etc. We in this paper developed a theoretical model of EM wave propagation perpendicular to a plasma slab with a one-dimensional density inhomogeneity along propagation direction to investigate essential characteristics of EM wave propagation in nonuniform plasmas. Particularly, the EM wave propagation in sub-wavelength plasma slabs, where the geometric optics approximation fails, is studied and in comparison with thicker slabs where the geometric optics approximation applies. The influences of both plasma and collisional frequencies, as well as the width of the plasma slab, on the EM wave propagation characteristics are discussed. The results can help the further understanding of propagation behaviours of EM waves in nonuniform plasma, and applications of the interactions between EM waves and plasmas.

WU Rong, WANG Chao, ZHANG Shang et al.

Based on the theory of magnetic resonance coupling, a model of three coil system for wireless power transmission was established, and the transmission efficiency formula was derived by applying equivalent circuit theory and two port network theory. Cross coupling coefficient between transmitting and receiving coils was analyzed theoretically by using the formula. The theoretical analysis results show that the transmission efficiency of the system should decrease if cross coupling coefficient is large enough. The simulation results verify the correctness of the theoretical analysis.

Xiaofeng Jiang, Hongsheng Xi

The optimization problem for the performance of opportunistic spectrum access is considered in this study. A user, with the limited sensing capacity, has opportunistic access to a communication system with multiple channels. The user can only choose several channels to sense and decides whether to access these channels based on the sensing information in each time slot. Meanwhile, the presence of sensing error is considered. A reward is obtained when the user accesses a channel. The objective is to maximize the expected (discounted or average) reward accrued over an infinite horizon. This problem can be formulated as a partially observable Markov decision process. This study shows the optimality of the simple and robust myopic policy which focuses on maximizing the immediate reward. The results show that the myopic policy is optimal in the case of practical interest.