一种新型自动化蓝莓采摘机的设计

针对小浆果种植领域中蓝莓收获难的问题,基于计算机软件和可编程序控制器,设计了一种新型自动化蓝莓采摘机。介绍了这一蓝莓采摘机的结构与工作原理,分析了控制方案,制作了样机,并进行了采摘试验。应用这一新型自动化蓝莓采摘机,可以满足蓝莓收获效率高、破损率低等要求。     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

Cultivated Wildness: Technodiversity and Wildness in Machines

This paper investigates the idea of cultivated wildness at the intersection of landscape design and artificial intelligence. The paper posits that contemporary landscape practices should overcome the potentially single understanding on wilderness, and instead explore landscape strategies to cultivate new forms of wild places via ideas and concerns in contemporary Environmental Humanities, Science and Technology Studies, Ecological Sciences, and Landscape Architecture. Drawing cases in environmental engineering, computer science, and landscape architecture research, this paper explores a framework to construct wild places with intelligent machines. In this framework, machines are not understood a layer of “digital infrastructure” that is used to extent localized human intelligence and agency. Rather machines are conceptualized as active agents who can participate in the intelligence of co-production. Recent developments in cybernetic technologies such as sensing networks, artificial intelligence, and cyberphysical systems can also contribute to establishing the framework. At the heart of this framework is “technodiversity,” in parallel with biodiversity, since a singular vision on technological development driven by optimization and efficiency reinforces a monocultural approach that eliminates other possible relationships to construct with the environment. Thus, cultivated wildness is also about recognizing “wildness” in machines.     

Affective Experience Measurement in Outdoor Environment via Machine Visual Cognition

Outdoor environments with quality landscapes can benefit people’s physical and mental health. Real-time assessment on individuals’ environmental affective experience can improve the scientism in measuring the quality of outdoor environments. Existing measurement methods are often insufficient for the cases of a larger site area or sample size. The machine visual cognition of Artificial Intelligence can realize the recognition of facial expressions and the changes in video images, which supports high-precision and long-cycle measurements on individuals’ affective experience in outdoor environments. Taking an urban community square as the study site, this research simultaneously collects participants’ facial data from video images and their electrodermal activity data, wherein Convolutional Neural Network algorithm model is trained with a deep learning algorithm, i.e. codec–SVM optimized model, whose reliability is tested through an additional experiment. The research reveals that: 1) The accuracy rate of the main and additional experiments in measuring individuals’ affective experience is 82.01% and 65.08%, respectively; 2) The additional experiment verifies the application potential of the codec–SVM optimized model; And 3) the model works more effective for outdoor scenarios with varying usage behaviors and open views. Therefore, machine visual cognition can be used for emotion measurement in a larger site area or sample size and contributes to the effectiveness of landscape optimization efforts, especially as an instrumental tool to study the affective experience of the ones who have communication or reading disability. The findings also demonstrate the model’s great potential in building Smart Cities with refined public services.     

Forecasting hydrogen production potential in islamabad from solar energy using water electrolysis

The focus of this study is the use of Machine Learning methods to forecast Solar Hydrogen production potential for the Islamabad region of Pakistan. For this purpose, we chose a Photovoltaic-Electrolytic (PV-E) system to forecast electricity and, hence, hydrogen production. The weather data used for forecasting and simulation were recorded with precise meteorological instruments stationed in Islamabad, over the course of 13 and a half months. Out of the three tested algorithms, Prophet performs the best with Mean Absolute Percentage Error of 3.7%, forecasting a daily average Hydrogen production of 93.3 × 10³ kg/Km². Although, the forecast in this study is made for the month of August and September, during which the local season moves towards winter, this study demonstrates solar hydrogen production, as a green energy source, has a tremendous potential in this region.     

Explicit and implicit measures of emotions: Data-science might help to account for data complexity and heterogeneity

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures - explicit to characterize a specific food, and implicit -physiological- to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research.     

Development of machine learning models to enhance element-doped g-C3N4 photocatalyst for hydrogen production through splitting water

Elemental doping has been widely adopted to enhance the photoactivity of graphitic carbon nitride (g-C₃N₄). Correlating photocatalytic performance with experimental conditions could improve upon the current trial-and-error paradigm, but it remains a formidable challenge. In this study, we have developed machine learning (ML) models to link experimental parameters with hydrogen (H₂) production rate over element-doped graphitic carbon nitride (D-g-C₃N₄). Material synthesis parameters, material properties, and H₂ production conditions are fed to the ML models, and the H₂ production rate is derived as the output. The trained ML models are effective in predicting the H₂ production rate using experimental data, as demonstrated by a satisfactory correlation coefficient for the test data. Sensitivity analysis is performed on input features to elucidate the ambiguous relationship between H₂ production rate and experimental conditions. The ML model can not only identify important features that are well-recognized and widely investigated in the literature, which supports the efficacy of the developed models but also reveals insights on less explored parameters that might also demonstrate significant impacts on photocatalytic performance. The method described in the present study provides valuable insights for the design of elemental doping strategies for g-C₃N₄ to improve the H₂ production rate without conducting time-consuming and expensive experiments. Our models may be used to revolutionize future catalyst design.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.