The Study of Usage of Hyperledger Fabric in Agricultural Ecommerce

With the advent of COVID-19, the ecommerce industry in India has seen an inflection point with rise in the demand across all the segments of the industry. Much ecommerce has been started to cater to the supply and demand mismatch in agricultural goods front. In these times, Blockchain is seen to create a trust bridge for all the stakeholder to transact through goods and supplies with minimal risks involved. But, to use this technology the technical, economical and a scalable approach to this technology is still a very prominent requirement for mass adoption. Permissionless Blockchain build on Proof of Work consensus protocol cannot be used due to their slow speed, low scalability, and high energy consumption for network functioning. This paper concludes with the possibility of using a Permissioned blockchain such as Hyperledger Fabric to not only solve the several underlying issues to facilitate efficiency in the Ecommerce architecture, with critical view on the mass adoption.

Implementation of Web Data Mining Technology Based on Python

With the arrival of the era of big data, people have gradually realized the importance of data. Data is not just a resource, it is an asset. This paper mainly studies the realization of Web data mining technology based on Python. This paper analyzes the overall architecture design of distributed web crawler system, and then analyzes in detail the principles of crawler’s URL function module, crawler’s web crawl function module, crawler’s web page parsing function module, crawler’s data storage function module and so on. Each function module of the crawler system was tested on the experimental computer, and the data information was summarized for comparative analysis. The main significance of this paper lies in the design and implementation of a distributed web crawler system, which, to a certain extent, solves the problems of slow speed, low efficiency and poor scalability of traditional single computer web crawler, and improves the speed and efficiency of web crawler in grasping information and web page data.

Characteristics of Storm Surge by Forward Speed of Typhoon in the South Coast of Korea

The damage caused by typhoons is gradually increasing due to the climate change recently. Hence, many studies have been conducted over a long period of time on various factors that determine the characteristics of storm surge, and most of relationships have been discovered. Because storm surge is complexly determined by various factors, it often show different results and draw different conclusions. For this reason, this study was conducted to understand the various characteristics of storm surge caused by changes in the forward speed of typhoons. This study was carried out with a numerical model, and the effect of forward speed could be analyzed by simplifying other factors as much as possible. When forward speed is increased, storm surges caused by typhoons tended to increase gradually. The storm surge showed a wide and gentle increase at a slow speed, but a narrow and steep one at a fast speed. In the case of the same forward speed, it was found that the storm surge was significantly influenced by the water depth of actual sea area. It was confirmed that the change in forward speed after passing Jeju Island did not significant affect on the storm surge in the south coast of Korea.

Facial Expression Recognition Method Combined with Attention Mechanism

Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Gear flank changes caused by wear do not only affect the dynamic behavior of gear systems, but they can also compromise the load-carrying capacity of gear teeth up to critical failure. To help avoid unintended consequences like downtime or safety risks, a condition monitoring system needs to be able to estimate the current wear during operation based on available sensor measurements. While many condition monitoring approaches in research rely on vibrational analysis with manual feature engineering, gearboxes running at slow speed do not reveal much excitation information for this purpose. We therefore introduce an approach for slow-speed gear wear monitoring that is based on the dynamic gear transmission error and that contains an automated feature selection process. For this purpose, we extract a large set of features from the preprocessed transmission error samples. Applying combined filter and embedded feature selection methods enables us to automatically identify and remove features with low relevance. The selection process consists of filtering features with no statistical dependence on the target wear value, removing redundant features with a correlation analysis and a recursive feature elimination process with cross-validation based on a random forest regressor. The remaining relevant set of features is the basis for model training and subsequent wear estimation. For this, the present research employed two independent ensemble models, random forest regression and gradient boosted regression trees. To train and test the proposed approach, we conducted slow-speed gear experiments with developing gear wear on a single-stage spur gear test rig setup. The results of both models show good gear wear estimation performance compared to the actual wear mass loss, even for small quantities. Hence, the proposed transmission error-based approach with automated feature selection is able to quantify the degree of slow-speed wear and offers a possible way for condition monitoring and fault diagnosis.

Measurement and Analysis of Torque Ripple in Inverter Driven Electric Machines

Torque ripple in electric machines can create both noise and vibration. While torque ripple is often well understood theoretically, it is much more difficult to accurately predict and measure. Often torque ripple is measured as a function of magnets and slot interaction at extremely low speed, but this can only be extrapolated to understand its implications for noise and vibration and is not useful for understanding torque response during dynamic scenarios like a change in load. The slow speed method of measurement also neglects possible switching effects on the torque profile. This paper will explore challenges in measuring the different sources of torque ripple and give an alternative method to measure torque ripple at higher speeds and also dynamically. This will include best practices and examples.