A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment

Spectrally resolved two-photon interference in a modified Hong-Ou-Mandel interferometer

A modified Hong-Ou-Mandel(HOM) interference reveals that the two-photon interference phenomenon can be explained only by the concept of a two-photon wave packet rather than the single-photon one. Previously, the measurements for such interference were usually performed in the time domain where the spectral information of the involved photons was integrated and lost during the measurement. Here, we theoretically explore the spectrally resolved two-photon interference for the modified HOM interferometer both in the cases of CW pump and pulse pump. It is found that, in the CW-pumped case, a one-dimensional (1D) temporal interferogram can be directly recovered by projecting a 2D spectrally resolved interferogram at different phases, without a standard delay-scanning. In the pulse-pumped case, the joint spectral intensity is phase-dependent and can be modulated by the time delay along the directions of both frequency sum and frequency difference between signal and idler photons, which may provide a versatile way to generate high-dimensional frequency entanglement and engineer high-dimensional quantum states. These results not only show more rich spectral information that cannot be extracted from the time domain, but also shed new light on a comprehensive understanding of the two-photon interference phenomenon in the frequency domain.Comment: 13 pages, 6 figure

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment

Identification and expression of the TIP subfamily in apple in response to drought stress

Abstract [Objective] This study aims to identify and analyze the members of the tonoplast intrinsic protein (TIP) subfamily in apple, and to investigate the expression patterns under drought stress. It provides information for further research on utilization of drought resistance gene resources in apple. [Methods] The MdTIPs in apple genome was identified by bioinformatics methods. The physicochemical properties, gene structures, conserved motifs, cis-regulatory elements, and phylogenetic trees, etc. of the subfamily members were analyzed. The expression patterns of MdTIPs in different organs under drought stress were analyzed by qRT-PCR. [Results] A total of 13 MdTIP genes were identified in the apple genome, and most members were localized at the plasma membrane. Chromosome localization analysis suggested that all members were distributed on 10 chromosomes, with 1-3 members on each chromosome. Besides, the promoter regions of the genes contained response elements for hormonal and adversity stresses. qRTPCR showed that MdTIP members were up-regulated in roots except MdTIP1;1, of which MdTIP1;3 and MdTIP1;4 were up-regulated 5.27 times and 5.69 times, respectively, compared with the control, suggesting that these two genes might be critical in response to drought stress. [Conclusion] Identification of MdTIP subfamily members is provided in this study. 10 MdTIP members are differentially expressed in roots, stems, and leaves, and 12 members are highly expressed in roots

Gobi agriculture: an innovative farming system that increases energy and water use efficiencies. A review

International audienceAbstractIn populated regions/countries with fast economic development, such as Africa, China, and India, arable land is rapidly shrinking due to urban construction and other industrial uses for the land. This creates unprecedented challenges to produce enough food to satisfy the increased food demands. Can the millions of desert-like, non-arable hectares be developed for food production? Can the abundantly available solar energy be used for crop production in controlled environments, such as solar-based greenhouses? Here, we review an innovative cultivation system, namely “Gobi agriculture.” We find that the innovative Gobi agriculture system has six unique characteristics: (i) it uses desert-like land resources with solar energy as the only energy source to produce fresh fruit and vegetables year-round, unlike conventional greenhouse production where the energy need is satisfied via burning fossil fuels or electrical consumption; (ii) clusters of individual cultivation units are made using locally available materials such as clay soil for the north walls of the facilities; (iii) land productivity (fresh produce per unit land per year) is 10–27 times higher and crop water use efficiency 20–35 times greater than traditional open-field, irrigated cultivation systems; (iv) crop nutrients are provided mainly via locally-made organic substrates, which reduce synthetic inorganic fertilizer use in crop production; (v) products have a lower environmental footprint than open-field cultivation due to solar energy as the only energy source and high crop yields per unit of input; and (vi) it creates rural employment, which improves the stability of rural communities. While this system has been described as a “Gobi-land miracle” for socioeconomic development, many challenges need to be addressed, such as water constraints, product safety, and ecological implications. We suggest that relevant policies are developed to ensure that the system boosts food production and enhances rural socioeconomics while protecting the fragile ecological environment

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment