Applied System Innovation

Big data have been used widely in many areas, including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted to improve the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized, and off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies.

Weiwei Jiang

Jiayun Luo

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

交通估计与预测的大数据：数据与工具综述

Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&amp;P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance—rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG (Facebook, Amazon, Apple, Netflix &amp; Google) equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.

Jaideep Singh

Matloob Khushi

Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating

特征学习在股票价格预测中显示出分析师评级的重要作用

In this study, we aimed to enhance the accuracy of product quality inspection and counting in the manufacturing process by integrating image processing and human body detection algorithms. We employed the SIFT algorithm combined with traditional image comparison metrics such as SSIM, PSNR, and MSE to develop a defect detection system that is robust against variations in rotation and scale. Additionally, the YOLOv8 Pose algorithm was used to detect and correct errors in product counting caused by human interference on the load cell in real time. By applying the image differencing technique, we accurately calculated the unit weight of products and determined their total count. In our experiments conducted on products weighing over 1 kg, we achieved a high accuracy of 99.268%. The integration of our algorithms with the load-cell-based counting system demonstrates reliable real-time quality inspection and automated counting in manufacturing environments.

Changhyun Lee

Yunsik Kim

Hunkee Kim

Computer-Vision-Based Product Quality Inspection and Novel Counting System

基于计算机视觉的产品质量检测与新型计数系统

Wind turbine design is an iterative process. Many aspects are considered when designing a wind turbine, including aerodynamic and power performance, structural loads and behavior, and control techniques. In the preliminary design stages, the governing equations of each design aspect are used to calculate the different loads and performance outputs while optimizing between them. This is usually made using wind turbine simulation software. This work presents a data-based machine learning (ML) approach towards the design of a micro-scale wind turbine. Extensive simulations are made on a 45 cm diameter rotor while performing parametric analysis using the QBlade wind turbine simulation tool. Different design parameters and wind conditions were changed one at a time, and data were collected to be further analyzed and used to train the ML models. The measurable outputs of the models are the coefficient of power (CP), loads normal and tangential to the blade at midspan (FN and FT), and the torque (T) on the rotor. Linear regression was found unsuitable for predicting CP due to its high nonlinearity; however, it gave satisfactory results for the blade loads. Ensemble models were found to give the highest accuracy for predicting all the desired outputs. The model accuracy is measured in terms of the coefficient of determination (R2), where the model could predict Cp, FN, FT, and T with R2 values of 0.999, 0.984, 0.984, and 0.986 respectively.

Raneem Mansour

Seifelden Osama

Hazem Ahmed

Mohamed Nasser

Norhan Mahmoud

Amira Elkodama

Amr Ismaiel

Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach

面向微型风力涡轮机设计的参数分析：一种机器学习方法

Sun glare poses a significant challenge in Advanced Driver Assistance Systems (ADAS) due to its potential to obscure important visual information, reducing accuracy in detecting road signs, obstacles, and lane markings. Effective sun glare mitigation and segmentation are crucial for enhancing the reliability and safety of ADAS. In this paper, we propose a new approach called “GCBAM-UNet” for sun glare segmentation using deep learning. We employ a pre-trained U-Net model VGG19-UNet with weights initialized from an ImageNet. To further enhance the segmentation performance, we integrated a Convolutional Block Attention Module (CBAM), enabling the model to focus on important features in both spatial and channel dimensions. Experimental results show that GCBAM-UNet is considerably better than other state-of-the-art methods, which will undoubtedly guarantee the safety of ADAS.

Nabila Zrira

Anwar Jimi

Mario Di Nardo

Issam Elafi

Maryam Gallab

Redouan Chahdi El Ouazzani

GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module

GCBAM-UNet：基于卷积块注意力模块的太阳光斑分割

The successful separation of a single sheet from a stack of paper is considered a paper-handling goal when using a robot hand. Under the condition of uncertain friction coefficients, a stochastic algorithm introducing randomness is formulated, which converges a paper stack to a state of single-sheet separation through the repetition of simple robot operations. This formulation is based on the proposed motion strategy for a robotic hand, which introduces a state of a partially separated paper bundle to temporarily allow the simultaneous separation of multiple sheets and a return operation to return the paper to the original paper bundle. The experimental results indicate that a single sheet can be completely separated from a vertically standing stack of business-card-sized papers by shifting the paper in a high-speed translational movement using two fingers of the robot hand that grasp the paper from both sides.

Taku Senoo

Atsushi Konno

Yuuki Yamana

Idaku Ishii

Single-Sheet Separation from Paper Stack Based on Friction Uncertainty Using High-Speed Robot Hand

基于摩擦不确定性的高速机器人手实现纸张堆单张分离

Developing and managing complex IoT–Edge–Cloud Continuum (IECC) systems are challenging due to the system complexity and diversity. Internet of Things (IoT), Edge, and Cloud components combined with artificial intelligence (AI) in data processing systems must ensure strong security and privacy for data sources. The approach of the IECC Data Management Framework (DMF) introduces a novel combination of multiple easy-to-configure plugin environments using data visualization features. These contributions collectively address the critical challenges inherent in heterogeneous environments such as scalability, data privacy, and configuration management by standardizing data flow configurations and increasing stakeholder trust in sensitive applications, particularly in critical infrastructure monitoring.

Janis Judvaitis

Eduards Blumbergs

Audris Arzovs

Andris Ivars Mackus

Rihards Balass

Leo Selavo

A Set of Tools and Data Management Framework for the IoT–Edge–Cloud Continuum

一套用于物联网-边缘-云连续体的工具和数据管理框架

Abbreviation	Appl. Syst. Innov.
Journal Impact	3.99
Quartiles(Global)	TELECOMMUNICATIONS(Q2)

ISSN	2571-5577
h-index	27

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2018，Proportion of original research papers: 87.80%，Self Citation Rate:2.60%， Gold OA Rate: 99.70%
Journal Fees	$Details
Average review cycle	网友分享经验：11 Weeks

Applied System Innovation

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Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating

Computer-Vision-Based Product Quality Inspection and Novel Counting System

Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach

GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module

Single-Sheet Separation from Paper Stack Based on Friction Uncertainty Using High-Speed Robot Hand

A Set of Tools and Data Management Framework for the IoT–Edge–Cloud Continuum

Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach

GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module

Computer-Vision-Based Product Quality Inspection and Novel Counting System