Search Results (104)

Background: The aim of this study was to develop an electrolysis system to produce silver nanoparticles free from toxic gases, as the most common reduction and electrolysis techniques produce nitrogen dioxide (NO₂) as a byproduct, which is harmful to human health. The new electrolysis system used two identical silver plate electrodes, replacing silver and carbon rods, and used water as the electrolyte instead of silver nitrate (AgNO₃) solution since AgNO₃ is the source of NO₂. Methods: The electrolytic silver nanoparticles (ESNs) produced by the new system were characterized and compared with reductive silver nanoparticles (RSNs). Using UV–Visible spectrophotometry, absorption peaks were found at 425 nm (ESN) and 437 nm (RSN). Using dynamic light scattering, the particle diameters were measured at 40.3 nm and 39.9 nm for ESNs at concentrations of 10 ppm and 30 ppm, respectively, and 74.0 nm and 74.6 nm for RSNs at concentrations of 10 ppm and 30 ppm, respectively. Antibacterial activity against Propionibacterium acnes (P. acnes) was assessed using the Kirby–Bauer method. Results: It was found that the efficacy of ESNs and RSNs was relatively lower than that of 5% chloramphenicol because it was measured in different concentration units (ESNs and RSNs in ppm and chloramphenicol in %). Using the calibration curve, the efficacy of 5% chloramphenicol was comparable to that of 0.005% ESN. It was also found that P. acnes developed a strong resistance to chloramphenicol and showed no resistance to ESNs. Conclusions: This finding underlines the tremendous potential of ESNs as a future antibiotic raw material. Full article

The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off between non-linearity and linear memory capacity is required for temporal pattern learning tasks. The more recent distance-based delay networks (DDNs) have shown improved memory capacity over ESNs in several benchmark temporal pattern learning tasks. However, it has not thus far been studied whether this increased memory capacity comes at the cost of reduced non-linear processing. In this paper, we advance the hypothesis that DDNs in fact achieve a better trade-off between linear MC and non-linearity than ESNs, by showing that DDNs can have strong non-linearity with large memory spans. We tested this hypothesis using the NARMA-30 task and the bitwise delayed XOR task, two commonly used reservoir benchmark tasks that require a high degree of both non-linearity and memory. Full article

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO₂ emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching $85\%$ compared to $80.88\%$ for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of $9.5$ s compared to $2.100$ s for the other model. Full article

Artificial intelligence (AI) has become an instrument used in all domains with good results. The water resources management field is not an exception. Therefore, in this article, we propose two machine learning (ML) techniques—an echo state network (ESN) and sparrow search algorithm–echo state network (SSA-ESN)—for monthly modeling of the water discharge of one of the biggest rivers in Romania for three periods (S, S1, and S2). In both models, R² was over 0.989 on the test and training sets and the mean absolute error (MAE) varied between 4.4826 and 7.6038. The performance of the SSA-ESN was similar, but the ESN had the shortest run time. The influence of anomalies on the models’ quality was assessed by running the algorithms on a series without the aberrant values, which were detected by the seasonal hybrid extreme studentized deviate (S-H-ESD) test. The results indicate that removing the anomalies significantly improved both models’ performance, but the run time was increased. Full article

Optimizing energy consumption is an important aspect of industrial competitiveness, as it directly impacts operational efficiency, cost reduction, and sustainability goals. In this context, anomaly detection (AD) becomes a valuable methodology, as it supports maintenance activities in the manufacturing sector, allowing for early intervention to prevent energy waste and maintain optimal performance. Here, an AD-based method is proposed and studied to support energy-saving predictive maintenance of production lines using time series acquired directly from the field. This paper proposes a deep echo state network (DeepESN)-based method for anomaly detection by analyzing energy consumption data sets from production lines. Compared with traditional prediction methods, such as recurrent neural networks with long short-term memory (LSTM), although both models show similar time series trends, the DeepESN-based method studied here appears to have some advantages, such as timelier error detection and higher prediction accuracy. In addition, the DeepESN-based method has been shown to be more accurate in predicting the occurrence of failure. The proposed solution has been extensively tested in a real-world pilot case consisting of an automated metal filter production line equipped with industrial smart meters to acquire energy data during production phases; the time series, composed of 88 variables associated with energy parameters, was then processed using the techniques introduced earlier. The results show that our method enables earlier error detection and achieves higher prediction accuracy when running on an edge device. Full article

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research. Full article

Sustainable nitrogen (N) fertilizer management practices in the Midwest U.S. strive to optimize crop production while minimizing N gas emission losses and nitrate-N (NO₃-N) losses in subsurface drainage water. A replicated site in upstate Missouri from 2018 to 2020 investigated the influence of different N fertilizer management practices on nutrient concentrations in drainage water, nitrous oxide (N₂O) emissions, and ammonia (NH₃) volatilization losses in a corn (Zea mays, 2018, 2020)–soybean (Glyince max, 2019) rotation. Four N treatments applied to corn included fall anhydrous ammonia with nitrapyrin (fall AA + NI), spring anhydrous ammonia (spring AA), top dressed SuperU and ESN as a 25:75% granular blend (TD urea), and non-treated control (NTC). All treatments were applied to subsurface-drained (SD) and non-drained (ND) replicated plots, except TD urea, which was only applied with SD. Across the years, NO₃-N concentration in subsurface drainage water was similar for fall AA + NI and spring AA treatments. The NO₃-N concentration in subsurface drainage water was statistically (p < 0.0001) lower with TD urea (9.1 mg L⁻¹) and NTC (8.9 mg L⁻¹) compared to fall AA + NI (14.6 mg L⁻¹) and spring AA (13.8 mg L⁻¹) in corn growing years. During corn years (2018 and 2020), cumulative N₂O emissions were significantly (p < 0.05) higher with spring AA compared to other fertilizer treatments with SD and ND. Reduced corn growth and plant N uptake in 2018 caused greater N₂O loss with TD urea and spring AA compared to the NTC and fall AA + NI in 2019. Cumulative NH₃ volatilization was ranked as TD urea > spring AA > fall AA + NI. Due to seasonal variability in soil moisture and temperature, gas losses were higher in 2018 compared to 2020. There were no environmental benefits to applying AA in the spring compared to AA + NI in the fall on claypan soils. Fall AA with a nitrification inhibitor is a viable alternative to spring AA, which maintains flexible N application timings for farmers. Full article

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far, it is still unclear why distance-based delay networks (DDNs) perform better than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have a greater total linear memory capacity, with the same amount of non-linear processing power. Full article

The purpose of this study is to determine the performances of luffa and greenhouse shading netting (which can be used as alternatives to commercial cellulose pads, that are popular for cooling greenhouses), the contribution of external shading to the evaporative cooling performance, and the energy consumption of the direct evaporative cooler. In this experiment, eight different applications were evaluated: natural ventilation (NV), natural ventilation combined with external shading net (NV + ESN), cellulose pad (CP), cellulose pad combined with external shading net (CP + ESN), luffa pad (LP), luffa pad combined with external shading net (LP + ESN), shading net pad (SNP), and shading net pad combined with external shading net (SNP + ESN). The cooling efficiencies of CP, CP + ESN, LP, LP + ESN, SNP, and SNP + ESN were found to be 37.6%, 45.0%, 38.9%, 41.2%, 24.4%, 29.1%, respectively. Moreover, their cooling capacities were 2.6 kW, 3.0 kW, 2.8 kW, 3.0 kW, 1.7 kW, 2.0 kW, respectively. The system water consumption values were 2.9, 3.1, 2.8, 3.2, 2.4, 2.4 l h⁻¹, respectively. The performance coefficients of the system were determined to be 10.2, 12.1, 11.3, 11.9, 6.6, 7.8. The system’s electricity consumption per unit area was 0.15 kWh m⁻². As a result of the study, it was determined that commercially used cellulose pads have advantages over luffa and shading net materials. However, luffa pads can be a good alternative to cellulose pads, considering their local availability, initial cost, cooling efficiency, and capacity. Full article

In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms. Full article

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications. Full article

This paper presents a novel data-driven approach for enhancing time series forecasting accuracy when faced with missing data. Our proposed method integrates an Echo State Network (ESN) with ARIMA (Autoregressive Integrated Moving Average) modeling, frequency decomposition, and online transfer learning. This combination specifically addresses the challenges missing data introduce in time series prediction. By using the strengths of each technique, our framework offers a robust solution for handling missing data and achieving superior forecasting accuracy in real-world applications. We demonstrate the effectiveness of the proposed model through a wind speed prediction case study. Compared to the existing methods, our approach achieves significant improvement in prediction accuracy, paving the way for more reliable decisionmaking in wind energy operations and management. Full article

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition. Full article

In the context of automatic charging for electric vehicles, collision localization for the end-effector of robots not only serves as a crucial visual complement but also provides essential foundations for subsequent response design. In this scenario, data-driven collision localization methods are considered an ideal choice. However, due to the typically high demands on the data scale associated with such methods, they may significantly increase the construction cost of models. To mitigate this issue to some extent, in this paper, we propose a novel approach for robot collision localization based on a sparse modular point matrix (SMPM) in the context of automatic charging for electric vehicles. This method, building upon the use of collision point matrix templates, strategically introduces sparsity to the sub-regions of the templates, aiming to reduce the scale of data collection. Additionally, we delve into the exploration of data-driven models adapted to SMPMs. We design a feature extractor that combines a convolutional neural network (CNN) with an echo state network (ESN) to perform adaptive feature extraction on collision vibration signals. Simultaneously, by incorporating a support vector machine (SVM) as a classifier, the model is capable of accurately estimating the specific region in which the collision occurs. The experimental results demonstrate that the proposed collision localization method maintains a collision localization accuracy of 91.27% and a collision localization RMSE of 1.46 mm, despite a 48.15% reduction in data scale. Full article

The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning process. However, the setting procedure for initializing the ESN’s recurrent structure may lead to difficulties in designing a sound reservoir that matches a specific task. This paper proposes an improved pre-training method to adjust the model’s parameters and topology to obtain an adaptive reservoir for a given application. Two strategies, namely global random selection and ensemble training, are introduced to pre-train the randomly initialized ESN model. Specifically, particle swarm optimization is applied to optimize chosen fixed and global weight values within the network, and the reliability and stability of the pre-trained model are enhanced by employing the ensemble training strategy. In addition, we test the feasibility of the model for time series prediction on six benchmarks and two real-life datasets. The experimental results show a clear enhancement in the ESN learning results. Furthermore, the proposed global random selection and ensemble training strategies are also applied to pre-train the extreme learning machine (ELM), which has a similar training process to the ESN model. Numerical experiments are subsequently carried out on the above-mentioned eight datasets. The experimental findings consistently show that the performance of the proposed pre-trained ELM model is also improved significantly. The suggested two strategies can thus enhance the ESN and ELM models’ prediction accuracy and adaptability. Full article