Developing the Smart Sorting Screw System Based on Deep Learning Approaches

The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in real-time. As a result, the AI technology could not be widely implemented in actual factory inspections. In this study, the proposed smart sorting screw system combines the internet of things technique and an anomaly network for detecting the defective region of the screw product. The proposed system has three prominent characteristics. First, the spiral screw images are stitched into a panoramic image to comprehensively detect the defective region that appears on the screw surface. Second, the anomaly network comprising of convolutional autoencoder (CAE) and adversarial autoencoder (AAE) networks is utilized to automatically recognize the defective areas in the absence of a defective-free image for model training. Third, the IoT technique is employed to upload the screw image to the cloud platform for model training and inference, in order to determine if the defective screw product is a pass or fail on the production line. The experimental results show that the image stitching method can precisely merge the spiral screw image to the panoramic image. Among these two anomaly models, the AAE network obtained the best maximum IOU of 0.41 and a maximum dice coefficient score of 0.59. The proposed system has the ability to automatically detect a defective screw image, which is helpful in reducing the flow of the defective products in order to enhance product quality.

Are auditors' reliance on conclusions from data analytics impacted by different data analytic inputs?

Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying audit-relevant information, auditors may not uniformly incorporate this information into their decision making. This study examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. nonfinancial), result in different auditors' decisions. Findings suggest that conclusions from data analytical models and data analyzed jointly impact budgeted audit hours. Specifically, when financial data is analyzed auditors increase budgeted audit hours more when predictive models are used than when anomaly models are used. The opposite occurs when nonfinancial data is analyzed, auditors increase budgeted audit hours more when anomaly models are used compared to predictive models. These findings provide initial evidence that data analytics with different inputs do not uniformly impact auditors' judgments.

Moving Average Filter untuk Memisahkan Efek Dangkal Anomali Gravitasi Time Lapse

[Title: Moving Average Filter for Time Lapse Gravity Anomaly Separation]. Interpretation of time-lapse gravity anomaly due to fluid injection in the reservoir is difficult when it mixed with shallow source anomalies. To solve the kind of problem, anomaly separation is performed with the method of Moving Average Filter. This study was conducted to obtain an effective method to separate the gravity anomaly of shallow sources on time-lapse gravity anomaly. Trials are conducted on anomaly models derived from reservoirs with three distinct depths that are 300, 600 and 900 meters. This forward model is then mixed with gravitational anomaly from the shallow source obtained from the field data. The mixed anomaly is then separated by a Moving Average filter. Results show that Moving Average filters can separate the shallow effect from the deep source anomaly and are effective up to a depth of 900 meters. The research is also beneficial for classroom learning in the computer programming class based on Matlab.

Mapping chromite deposits with audio magnetotellurics in the Luobusa ophiolite of southern Tibet

The exploration of podiform chromites in the Indus Yarlong Zangbo suture zone of southern Tibet has proved difficult because most known deposits pinch out and then reappear in the same direction. Several ground-based geophysical approaches such as gravity, magnetic, and controlled-source audio-frequency magnetotelluric (CSAMT) methods have been applied to explore for these chromite deposits but have mostly failed to delineate prospective areas. We have evaluated a successful podiform chromite exploration case history that is based on AMT. More than 8000 AMT stations were used in this study within a [Formula: see text] area of the ophiolite belt. Line separations were 80 or 40 m, and the station separation was 20 m. We implemented Bostick conversion and nonlinear conjugate gradient inversions for data interpretation, whereas 2D resistivity sections and 3D resistivity imaging were used to elucidate the inner structure and distribution of rock faces within the Luobusa ophiolite. Results from rock physics and drilling further indicate that resistivity-anomaly domains from these AMT results are correlated with rock faces in terms of fresh harzburgite, altered harzburgite and dunite, and they can thus be connected to concealed deposits. Therefore, we have developed three resistivity-anomaly models for chromite exploration, and we delineated several prospective regions containing exploitable deposits within the Luobusa ophiolite. Seven of the nine verified boreholes discussed in this paper intersected with chromite deposits; one comprises the largest and highest grade chromite deposit in China to date. Our AMT results provide the impetus for future chromite exploration in Tibet and enable a refined understanding of the structure and distribution of rock faces within the Luobusa ophiolite.