A Supply Response Model for Rice and Soybeans in the Mississippi Delta.

This study examines the economic sources of variation in the supply of rice and soybeans in the Mississippi Delta. As farmers alter their land allocations to various crops, they cause fluctuations in the supply of agricultural products. Traditional theory posits that acreage planted is a function of lagged prices, but does not explicitly recognize some technological constraints. One prevalent technological constraint is that the monocropping of particular crops results in a significant decrease in land productivity. The model in this study attempts to improve the specification of the technological constraint of declining soil productivity due to monocropping. Two different statistical methods were used to estimate the system of equations derived from the mathematical model. The empirical results depend upon the statistical technique. The technique involving the fewest restrictions on parameters supports the adjustment costs concept associated with the traditional models. When certain restrictions are placed on the parameters of the price process, which also appear in the decision rule, the results support the hypotheses of this study. The first hypothesis is that declining soil productivity plays a significant role in farmers\u27 land allocation decisions. The second hypothesis is that farmers optimally allocate their land in each time period

CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Machine breakdowns in industrial plants cause production delays and financial damage. In the era of cyber-physical systems, ma- chines are equipped with a variety of sensors to monitor their status. For example, changes to sensor values might indicate an abnormal behav- ior and, in some cases, detected anomalies can be even used to predict machine breakdowns. This procedure is called predictive maintenance, which pursues the goal to increase machine productivity by reducing down times. Thereby, anomalies can be either detected by training data models based on historic data or by implementing a self-learning ap- proach. In this work, the use of neural networks for detecting anomalies is evaluated. In the considered scenarios, anomaly detection is based on temperature data from a press of a machine manufacturer. Based on this, a framework was developed for dfferent types of neural networks as well as a high-order linear regression approach. We use the proposed neural networks for restoring missing sensor values and to improve over- all anomaly detection. An evaluation of the used techniques revealed that the high-order linear regression and an autoencoder constitute best practices for data recovery. Moreover, deep neural networks, especially convolutional neural networks, provide the best results with respect to overall anomaly detection

Process-Driven and Flow-Based Processing of Industrial Sensor Data

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results

Towards a Hierarchical Approach for Outlier Detection in Industrial Production Settings

In the context of Industry 4.0, the degree of cross-linking between machines, sensors, and production lines increases rapidly.However, this trend also offers the potential for the improve-ment of outlier scores, especially by combining outlier detectioninformation between different production levels. The latter, in turn, offer various other useful aspects like different time series resolutions or context variables. When utilizing these aspects, valuable outlier information can be extracted, which can be then used for condition-based monitoring, alert management, or predictive maintenance. In this work, we compare different types of outlier detection methods and scores in the light of the aforementioned production levels with the goal to develop a modelfor outlier detection that incorporates these production levels.The proposed model, in turn, is basically inspired by a use casefrom the field of additive manufacturing, which is also known asindustrial 3D-printing. Altogether, our model shall improve the detection of outliers by the use of a hierarchical structure that utilizes production levels in industrial scenarios

Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling

In the context of the Industrial Internet of Things (IIoT), image and object recognition has become an important factor. Camera systems provide information to realize sophisticated monitoring applications, quality control solutions, or reliable prediction approaches. During the last years, the evolution of smart glasses has enabled new technical solutions as they can be seen as mobile and ubiquitous cameras. As an important aspect in this context, the recognition of objects from images must be reliably solved to realize the previously mentioned solutions. Therefore, algorithms need to be trained with labeled input to recognize differences in input images. We simplify this labeling process using voice commands in Mixed Reality. The generated input from the mixed- reality labeling is put into a convolutional neural network. The latter is trained to classify the images with different objects. In this work, we describe the development of this mixed-reality prototype with its backend architecture. Furthermore, we test the classification robustness with im- age distortion filters. We validated our approach with format parts from a blister machine provided by a pharmaceutical packaging company in Germany. Our results indicate that the proposed architecture is at least suitable for small classification problems and not sensitive to distortions

Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings

To build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly

Analysis of Fuel Cells Utilizing Mixed Reality and IoT Achievements

Recent advances in the development of smart glasses enable new interaction patterns in an industrial context. In the field of Mixed Reality, in which the real world and virtual objects fuse, new developments allow for advanced procedures of condition monitoring. Hereby, the smart glasses serve as a mobile display and inspection station. In this work, we focus on the applicability of Mixed Reality to monitor data of the spatially resolved current density distribution of a fuel cell. To be more specific, we implemented an IoT approach based on the Message Queuing Telemetry Transport protocol (MQTT) to enable the aforementioned monitoring. The realized solution, in turn, provides a live monitoring as well as an overview feature

Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data

Visual analytics are becoming more and more important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments like smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional data sets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production data set in order to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that shall support manufactures as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype shall simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides a (1) correlation coefficient graph, a (2) plot for the information loss, and a (3) 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered as being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources by the use of smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data on a daily basis. Moreover, it was reported that such system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains like medicine