Data Collection Equipment Research Articles

Improved Pavement Design Through Pavement Instrumentation

Past methods of pavement design have relied heavily on the use of empirical data and laboratory testing to develop the materials and thickness required for an estimated number of loadings. The technology has advanced to the point where instrumentation can be used to measure the effect of the existing traffic flow on the pavement under various climatic conditions. This project is part of an FHWA Demonstration project and an Iowa Highway Research Board project on pavement instrumentation. The paper describes the instrumentation that has been developed to monitor the traffic effect on a section of I-80 in southwestern Iowa. The instrumentation includes the use of piezo electric cables to trigger the data collection and monitor the weight, speed, lateral location and lane placement of the vehicles passing over the data collection site. Data collection equipment includes concrete deflection, concrete and dowel bar strain, and temperature of the pavement and subgrade. A special application of nuclear gages is being used to measure the moisture and density of the material under the pavement at the joints and midslab location. Examples of the results are shown. The site is equipped with telephone lines and computer capability to allow remote sensing of the results at an office location at the University. Site planning and installation problems and solutions are discussed in this report. The successes and failures of the installation portion of the project are discussed. This type of location allows the designer and planner to monitor traffic and pavement performance remotely without interference or identification by the traffic to improve the knowledge of pavement/traffic/environment interaction.

Information System for Data Collection on Building Tools and Materials Using the Rational Unified Process (RUP) Method

UD. FLORA records the re-inventory of building equipment that has been sold by reading the transactions one by one in the ledger. A proper way is needed to overcome the problems that occur. This research aims to create an information system software. In making information system software, a method is needed that can carry out the stages from initial design to implementation of the software. This research uses the Rational Unified Process (RUP) method, namely a software development approach that is carried out repeatedly (iterative), focuses on architecture (architecture-centered), is more directed based on use cases (use case driven). This research produces a tool data collection application at UD. FLORA using the RUP method. The existence of an information system for data collection on building equipment using the Rational Unified Process method means that UD. FLORA can be helped in managing building equipment data collection. The system enables real-time transaction recording, providing better insight into inventory status, and supporting more accurate decision-making.

The effect of chiropractic spinal manipulation on the H-reflex and muscle strength in children with cerebral palsy: A feasibility study

Background Children with cerebral palsy (CP) have deficits in various aspects of motor control, including motor neuron excitability, which can affect muscle strength, gait, and ability to perform activities of daily living. Previous research on chiropractic spinal manipulation in healthy adults, athletes, and a brain-injured population has indicated improvements in motor neuron excitability, muscle strength, and various aspects of motor control. Thus, chiropractic spinal manipulation may improve motor control in children with CP. Methods Children with spastic diplegic CP, aged 8-13 years, were recruited for a randomized, controlled feasibility study. Feasibility was assessed in terms of recruitment strategy and rate, data collection procedures, equipment, intervention, and compliance. Results Three children completed the data collection comprising pre- and post-measurements of the H reflex, V-wave, and muscle strength. The recruitment strategy and rate were not feasible. The data collection procedures were appropriate and complied with, except for the V-wave measurements. The H-reflex threshold decreased and s50 and slope increased in the participants who received chiropractic spinal manipulation; the opposite was seen in the control group. The changes in MVC force were inconsistent between subjects. Conclusion This study answered some important feasibility questions regarding conducting a large-scale randomized controlled study with the same design. Some aspects proved feasible, such as H-reflex recordings, and some aspects, such as recruitment methods and V-wave recordings, need to be altered for future research in this area.

Prediction of flashover time in a compartment fire by CNN-LSTM based deep neural network considering wearable data collection equipment

Prediction of flashover time in a compartment fire by CNN-LSTM based deep neural network considering wearable data collection equipment

Horseshoe effects on equine gait-A systematic scoping review.

To provide an overview of available research about effects of horseshoes on equine kinetics and kinematics. The terms, "horse/equine," "hoof," "shoes/horseshoes," "kinetics," and "kinematics" were searched in PubMed, Web of Science, Center for Agriculture and Bioscience International, and United States Department of Agriculture National Agricultural Library for manuscripts from first availability to 2024. Independent reviewers used preferred reporting items for systematic reviews and meta-analyses guidelines to map and extract evidence-based data from sources. Metrics included participant demographics, research methods, major findings, and study limitations. Knowledge gaps were also identified. A total of 46 studies were included. Most utilized non-lame horses to compare original shoe designs or modifications to unshod or a standard open-heel shoe. Horse demographics and gait, study design and outcome measures varied widely. Prevalent data collection equipment included force platforms and pressure plates, wearable force measuring technology, and videography. Many studies reported shoeing effects on limb forces and motion, but there was little consensus among unrelated studies. Common limitations included insufficient data resolution, no randomization, small sample size, single breed, and outcome measures specific or unique to the study. Knowledge gaps included data collection from all limbs and the impact of conformation and limb and hoof morphology and health condition on outcomes. Information from manuscripts that met inclusion criteria confirmed distinct, variable effects of shoe characteristics on equine gait parameters. Details from published work can serve as resource for clinical decisions and to guide standardization among investigations on shoe configuration effects on equine motion.

Revitalizing Government Internal Auditor Apparatus in the Era of Industry 4.0

The purpose of this paper is to propose a transformation strategy which is the focus of the Government Internal Supervisory Apparatus (APIP) on the impact of Industry 4.0 on the internal audit function and the development of an improvement plan for the Internal Auditor SPIP level . can be applied to address audit challenges in the Industry 4.0 era. Due to the impact of Industry 4.0 on audit activities, APIP is required to be able and quickly adapt to technological advances. The adoption of Industry 4.0 technology positions APIP as an activity analyst who can identify errors in business processes and devise solutions to fix them. As a consequence, an APIP transformation strategy is needed to face the challenges of Industry 4.0. The author suggests that the APIP transformation strategy to increase the SPIP level must include: (1) the use of data collection equipment, such as sensors and software, to collect data; (2) inclusion of characteristics that characterize APIP's ability to complete audit tasks, including cognitive abilities, problem solving abilities, communication skills, internal control systems, and agility; and (3) applying a professional, millennial, innovative approach that prioritizes integrity, objectivity and professionalism while still embracing the possibility of failure.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey.

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

Learning Universal Trajectory Representation via a Siamese Geography-Aware Transformer

With the development of location-based services and data collection equipment, the volume of trajectory data has been growing at a phenomenal rate. Raw trajectory data come in the form of sequences of “coordinate-time-attribute” triplets, which require complicated manual processing before they can be used in data mining algorithms. Current works have started to explore the emerging deep representation learning method, which maps trajectory sequences to vector space and applies them to various downstream applications for boosting accuracy and efficiency. In this work, we propose a universal trajectory representation learning method based on a Siamese geography-aware transformer (TRT for short). Specifically, we first propose a geography-aware encoder to model geographical information of trajectory points. Then, we apply a transformer encoder to embed trajectory sequences and use a Siamese network to facilitate representation learning. Furthermore, a joint training strategy is designed for TRT. One of the training objectives is to predict the masked trajectory point, which makes the trajectory representation robust to low sampling rates and noises. The other is to distinguish the difference between trajectories by means of contrastive learning, which makes the trajectory representation more uniformly distributed over the hypersphere. Last, we design a benchmark containing four typical traffic-related tasks to evaluate the performance of TRT. Comprehensive experiments demonstrate that TRT consistently outperforms the state-of-the-art baselines across all tasks.

Best Practices and Recommendations for Research Using Virtual Real-Time Data Collection: Protocol for Virtual Data Collection Studies.

The COVID-19 pandemic and the subsequent need for social distancing required the immediate pivoting of research modalities. Research that had previously been conducted in person had to pivot to remote data collection. Researchers had to develop data collection protocols that could be conducted remotely with limited or no evidence to guide the process. Therefore, the use of web-based platforms to conduct real-time research visits surged despite the lack of evidence backing these novel approaches. This paper aims to review the remote or virtual research protocols that have been used in the past 10 years, gather existing best practices, and propose recommendations for continuing to use virtual real-time methods when appropriate. Articles (n=22) published from 2013 to June 2023 were reviewed and analyzed to understand how researchers conducted virtual research that implemented real-time protocols. "Real-time" was defined as data collection with a participant through a live medium where a participant and research staff could talk to each other back and forth in the moment. We excluded studies for the following reasons: (1) studies that collected participant or patient measures for the sole purpose of engaging in a clinical encounter; (2) studies that solely conducted qualitative interview data collection; (3) studies that conducted virtual data collection such as surveys or self-report measures that had no interaction with research staff; (4) studies that described research interventions but did not involve the collection of data through a web-based platform; (5) studies that were reviews or not original research; (6) studies that described research protocols and did not include actual data collection; and (7) studies that did not collect data in real time, focused on telehealth or telemedicine, and were exclusively intended for medical and not research purposes. Findings from studies conducted both before and during the COVID-19 pandemic suggest that many types of data can be collected virtually in real time. Results and best practice recommendations from the current protocol review will be used in the design and implementation of a substudy to provide more evidence for virtual real-time data collection over the next year. Our findings suggest that virtual real-time visits are doable across a range of participant populations and can answer a range of research questions. Recommended best practices for virtual real-time data collection include (1) providing adequate equipment for real-time data collection, (2) creating protocols and materials for research staff to facilitate or guide participants through data collection, (3) piloting data collection, (4) iteratively accepting feedback, and (5) providing instructions in multiple forms. The implementation of these best practices and recommendations for future research are further discussed in the paper. DERR1-10.2196/53790.

Filling of missing values of voltage data based on ResIOFNN

With the increasing scale of power Systems, the amount of data that needs to be collected increases exponentially, and data collection equipment will inevitably experience different degrees of data loss. Traditional missing data filling algorithms, such as Expectation Maximization Algorithm (EM) and K Nearest Neighbors (KNN), have low accuracy when dealing with missing data. Given the limitations of the current time series data-filling model, this paper combines the idea of deep learning technology. It proposes an improved voltage missing value-filling algorithm based on the Fourier neural network model. The model can combine the future and past information of the missing data to complete the filling work on the missing data set, which improves the precision of voltage data filling. The calculation example adopts the data of the real power grid for simulation analysis, and the calculation outcomes prove the data-filling method’s high level of fill accuracy.

Reinforcement learning methods for network-based transfer parameter selection

A significant challenge in self-driving technology involves the domain-specific training of prediction models on intentions of other surrounding vehicles. Separately processing domain-specific models requires substantial human resources, time, and equipment for data collection and training. For instance, substantial difficulties arise when directly applying a prediction model developed with data from China to the United States market due to complex factors such as differing driving behaviors and traffic rules. The emergence of transfer learning seems to offer solutions, enabling the reuse of models and data to enhance prediction efficiency across international markets. However, many transfer learning methods require a comparison between source and target data domains to determine what can be transferred, a process that can often be legally restricted. A specialized area of transfer learning, known as network-based transfer, could potentially provide a solution. This approach involves pre-training and fine-tuning "student" models using selected parameters from a "teacher" model. However, as networks typically have a large number of parameters, it raises questions about the most efficient methods for parameter selection to optimize transfer learning. An automatic parameter selector through reinforcement learning has been developed in this paper, named "Automatic Transfer Selector via Reinforcement Learning". This technique enhances the efficiency of parameter selection for transfer prediction between international self-driving markets, in contrast to manual methods. With this innovative approach, technicians are relieved from the labor-intensive task of testing each parameter combination, or enduring lengthy training periods to evaluate the impact of prediction transfer. Experiments have been conducted using a temporal convolutional neural network fully trained with the data from the Chinese market and one month's US data, focusing on improving the training efficiency of specific driving scenarios in the US. Results show that the proposed approach significantly improves the prediction transfer process.

IoT 기반 탄소중립 모듈형 스마트 온실 구현

Recently, in digital agriculture, the types and utilization of greenhouses based on IoT are spreading, and greenhouses are being modernized, enlarged, and even factoryized using smart technology. However, a specific standardization plan has not been proposed according to the equipment for data collection in the smart greenhouse and the size or shape of the greenhouse. In other words, there is a lack of standard data for facility equipment, such as the type and number of sensors and equipment according to the size of the greenhouse, the type of greenhouse construction film and materials suitable for crops and carbon neutrality. Therefore, in this study, the suitability of the implementation, installation and quantity of IoT equipment for data collection was tested, and some standard technologies were presented through the implementation of data collection and communication methods. In addition, impact strength, tensile, tear, elongation, light transmittance, and lifespan issues for PE, PVC, and EVA, which account for about 90% of existing greenhouses, were presented, and the shape, size, and environmental problems of greenhouses made of films were presented. presented in the text. In this research paper, a standardized carbon-neutral modular smart greenhouse using nano-material film was implemented as a solution to environmental problems such as greenhouse size, farm crop type, greenhouse lifespan, and film, and its performance with existing greenhouses was analyzed and presented. Through this, we propose a modularized greenhouse that can be expanded or reduced freely without distinction in the size of the greenhouse or the shape of farmhouse crops, and the lifespan is extended and standardized. Finally, the average characteristics of greenhouses using existing PE, PVC, and EVA films and the characteristics of greenhouses using new carbon-neutral nanomaterials are compared and reviewed, and a plan to implement an expandable IoT greenhouse that supports carbon neutrality is proposed.

Shopee Pay Free Shipping Promotion Moderation On Customer Satisfaction

The reason for this observation become to partly and concurrently decide the impact of service quality on customer satisfaction in Tangerang city, and to decide the variable of free shipping promotion as a moderator variable of service quality on customer satisfaction. Pattern length changed into determining the usage of a purposive sampling technique with a minimum of 96 respondents. The lowest population for this has an observed is her Shoppe customers in Tangerang city who take gain of free shipping on their Shopee purchases. This observation is an associative causal test. data collection equipment is inside the shape of digital surveys distributed thru Google forms. data analysis techniques the use of the JASP 0.16.4 software program. The service exceptional results had a massive advantageous effect on client satisfaction with a t-statistic of 6,610, and the free shipping marketing campaign payment promoting had a good sized impact on customer satisfaction with a t-statistic of 2,083 from the carrier high-quality survey and free delivery promotion impact. Make a big nice impact. The F-test result is 72,936, which has a big positive effect on customer satisfaction. A Sobel takes a look at the rating of 2.027 > 1.986 indicating that the free delivery advertising variable is a moderator of quality of service variables related to customer satisfaction. Based on the results with a coefficient of determination of 0.601, it suggests that distinct Shopee promotions on free shipping and provider quality can explain 60.1% of his Shopee customer satisfaction in Tangerang city.

E-Commerce Platform: Free Shipping Promotion Moderation on Customer Satisfaction

The reason for this observation is to partly and concurrently decide the impact of service quality on customer satisfaction in Tangerang city and the variable of free shipping promotion as a moderator variable of service quality on customer satisfaction. Pattern length changed into determining the usage of a purposive sampling technique with a minimum of 96 respondents. The lowest population for this has an observed is her Shoppe customers in Tangerang city, who take gain of free shipping on their Shopee purchases. This observation is an associative causal test. Data collection equipment is inside the shape of digital surveys distributed thru Google forms. Data analysis techniques use of the JASP 0.16.4 software program. The exceptional service results had a massive advantageous effect on client satisfaction with a t-statistic of 6,610, and the free shipping marketing campaign promotion had a good-sized impact on customer satisfaction with a t-statistic of 2,083 from the high-quality carrier survey and free delivery promotion impact. Make an incredibly significant impact. The F-test result is 72,936, which significantly positively affects customer satisfaction. A Sobel looks at the rating of 2.027 > 1.986, indicating that the free delivery advertising variable moderate’s quality of service variables related to customer satisfaction. The results with a coefficient of determination of 0.601 suggest that distinct Shopee promotions on free shipping and provider quality can explain 60.1% of his Shopee customer satisfaction in Tangerang city.

Recognition of Underwater Materials of Bionic and Natural Fishes Based on Blue-Green Light Reflection.

Thanks to the advantages of low disturbance, good concealment and high mobility, bionic fishes have been developed by many countries as equipment for underwater observation and data collection. However, differentiating between true and bionic fishes has become a challenging task. Commonly used acoustic and optical technologies have difficulty in differentiating bionic fishes from real ones due to their high similarity in shape, size, and camouflage ability. To solve this problem, this paper proposes a novel idea for bionic fish recognition based on blue-green light reflection, which is a powerful observation technique for underwater object detection. Blue-green light has good penetration under water and thus can be used as a signal carrier to recognize bionic fishes of different surface materials. Three types of surface materials representing bionic fishes, namely titanium alloy, carbon fiber, and nylon, are investigated in this paper. We collected 1620 groups of blue-green light reflection data of these three kinds of materials and for two real fishes. Following this, three machine learning algorithms were utilized for recognition among them. The recognition accuracy can reach up to about 92.22%, which demonstrates the satisfactory performance of our method. To the best of our knowledge, this is the first work to investigate bionic fish recognition from the perspective of surface material difference using blue-green light reflection.

Effect of space for runway roughness evaluation

ABSTRACT This study was undertaken to develop a new index, the Airport Runway Roughness Index (ARRI), that considers the effect of runway space when evaluating airport runway roughness. The difference between roadway and runway roughness evaluations is analysed in detail and the spatial effect is considered for a proposed runway roughness evaluation model. The evaluation of runway roughness should fully account for the dynamic responses of different typical aircraft during taxiing at the most unfavourable speed for the full cross-section of the runway. ARRI is defined according to the normalised weighted average of the vibration responses of typical aircraft models at the most unfavourable excitation waveband, the most unfavourable taxiing speed, and the most unfavourable lateral offset position within the longitudinal distance of the runway. Using runway three-dimensional full-section unevenness data collection equipment, a runway roughness evaluation procedure that is based on ARRI is proposed. The ARRI system provides qualitative improvement in terms of its representation ability, the rationality of the evaluation criteria, and the correlation of the indicators.

Automated phosphor thermometry lifetime calibration of multiple phosphors and emission lines to above 1900 K

A method is specified which enables lifetime calibration of multiple phosphors and emission lines at the same time to temperatures above 1900 K. The experimental setup and algorithm used for data collection and experimental equipment control are described. The phosphors were coated on an alumina oxide disc and the reference temperature was measured using three type B thermocouples. The algorithm automates the data collection process such that no input from an operator is required during operation. The potential systematic error in calibration temperature was evaluated and was less than 1% around 1400 K.

IMPROVING VOCABULARY MASTERY AND DISTINGUISHING VOCABULARY THROUGH LISTENING TO AUDIO-VISUAL

This research design is from an experimental study. The goal of this research is to see if using audio-visual aids in increasing student vocabulary. The pretest and prost test are data collection equipment. The exam was intended to collect data in order to determine the impact of audio-visual use. The results showed that: 1. The use of audio visual can increase the number of students' abilities in listening to dialogue, 2. Students can distinguish vocabulary with the same pronunciation but different meanings. That ability was increase, after learning to use audio visual. They are better in their listening to audio.

IMPROVING VOCABULARY MASTERY AND DISTINGUISHING VOCABULARY THROUGH LISTENING TO AUDIO-VISUAL

This research design is from an experimental study. The goal of this research is to see if using audio-visual aids in increasing student vocabulary. The pretest and prost test are data collection equipment. The exam was intended to collect data in order to determine the impact of audio-visual use. The results showed that: 1. The use of audio visual can increase the number of students' abilities in listening to dialogue, 2. Students can distinguish vocabulary with the same pronunciation but different meanings. That ability was increase, after learning to use audio visual. They are better in their listening to audio.

Formulating emergency operation posts: lesson learned from Palu 2018 and Bantul 2006 indonesian earthquake events

Abstract Emergency operation posts play significant roles during emergency response. Unprepared emergency operation posts are likely to lead to poor coordination, a chaotic evacuation process, and uneven aids distribution, worsening disaster impacts. Reflecting on earthquake events in Indonesia, this paper examines emergency operation posts and equipment needed in those emergency operation posts during disaster events to improve emergency management. The present paper investigates government regulations and performs Focus Group Discussions (FGDs) in Palu, Bantul, and Surabaya to achieve the research goal. This study finds that six emergency operation posts, including Command Post, Field Post, Support Post, Associate Post, Communal Tent, and Individual Tent, are required to improve evacuation processes, multi-sectoral coordination, and aid distributions. Finally, the present study suggested that each post must be facilitated with adequate equipment, categorized into basic equipment, data collection equipment, and coordination equipment to support emergency response activities.