Multi-channel Data Collection Research Articles

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

Enterprise asset management and accounting using the Internet of Things and big data

With the rapid development of information technology, the Internet of Things (IoT) and big data have gradually become the core tools of modern enterprise management and decision-making. This study explores the application and potential value of IoT and big data in the field of enterprise assets and accounting. Through strategic questionnaire design and multi-channel data collection, a corresponding data model is constructed, and the effectiveness of the model is further verified. The results show that these technologies can not only improve the management efficiency of enterprises, but also provide more accurate and timely data support for corporate decision-making. However, in practical applications, enterprises also need to face data integrity, technology updates, and a host of other challenges. This study not only provides enterprises with an opportunity to gain an in-depth understanding of these technologies, but also provides valuable implications for future research and applications.

Variational inference-based transfer learning for profile monitoring with incomplete data

Profile monitoring is a widely used tool in quality control. The rapid development of sensor technology has created unprecedented opportunities for multi-channel profile data collection, which motivates the modeling and transfer learning for multi-profile data. The Multi-output Gaussian Process (MGP) is often used for multi-profile data, due to its flexible modeling capability and elegant mathematical properties. However, two practical concerns limit the broader application of MGP for transfer learning and monitoring of multi-profile data: high computational complexity and data incompleteness. In this article, we propose a Variational Inference (VI)-based MGP framework to facilitate transfer learning and profile monitoring using incomplete profile data. The proposed framework features a specially designed convolutional structure for constructing an explicit covariance relationship between the inducing variables in VI and the MGP in multi-profile data. This structure inspires a comprehensive solution to both computational complexity and data incompleteness in modeling multi-profile data, which facilities the transfer learning for profile monitoring. Various numerical studies and one real case study are conducted to demonstrate and compare the transfer learning and monitoring performance of the proposed method. The results show the method can achieve superior monitoring performance while maintain a very low level of computational complexity.

Intelligent Sensing and Monitoring System for High-Voltage Transmission Line Status of Smart Grid Based on IoT Technology

This paper integrates the Internet of Things (IoT) technology and a smart grid to build an electric power IoT architecture and analyzes the intelligent sensing technology and wireless communication technology in this electric power IoT. Through the multi-channel data collection technology in power IoT technology and an orthogonal discrete multiwavelet transform algorithm of edge computing technology, the high-voltage transmission line status data of the smart grid are collected and processed. Then, the high-voltage transmission line condition monitoring system is designed using the node design of the high-voltage transmission line condition monitoring sensing network and the optimal sensor configuration for droop monitoring. The performance of the monitoring system is simulated and examined. The experimental results show that as the number of burst data nodes increases, the acceptance rate of the ODMT algorithm decreases from 99% to 98%, and the network survival time is over 2000. When the current exceeds 20% of the rated current, the overall measurement error is controlled at approx. 3%. At a height of 4 m, the ratio of the difference between the input voltage and output voltage sensing monitoring is approx. 5%. The error range of temperature sensing monitoring is within ±1 °C. The error rate of communication distance within 200 m is 0, and over 200 m, the error rate is approx. 7%. This system can monitor the transmission status of high-voltage lines very well.

A Novel Blockchain-Based Model for Agricultural Product Traceability System

At present, the application of blockchain in the traceability system has become a mainstream technology to ensure the quality and safety of agricultural products. However, blockchain is designed to ensure open and transparent data and tamper-proof, and there is a deficiency in the privacy protection of data. In this study, a novel blockchain-based model for agricultural product traceability system is proposed, which has good generality and can protect the privacy of the agricultural product information in the traceability system. First, we designed a multi-channel data collection and uploading architecture, with one channel corresponding to the collection and uploading of information of one type of agricultural product, so as to meet the actual traceability needs of various types of agricultural products. Then, the information of agricultural products uploaded by each channel is encrypted by using the hierarchical encryption algorithm, and the encrypted information is stored in the blockchain to realize the secure sharing of agricultural product information. Finally, we evaluated the system model on the Hyperledger Fabric platform.

Balanced Multi-Channel Data Collection in Wireless Sensor Networks

Data collection is an essential task in Wireless Sensor Networks (WSNs). In data collection process, the sensor nodes transmit their readings to a common base station called Sink. To avoid a collision, it is necessary to use the appropriate scheduling algorithms for data transmission. On the other hand, multi-channel design is considered as a promising technique to reduce network interference and latency of data collection. This technique allows parallel transmissions on different frequency channels, thus time latency will be reduced. In this paper, we present a new scheduling method for multi-channel WSNs called Balanced Multi Channel Data Collection (Balanced MC-DC) Algorithm. The proposed protocol is based on using both Non-Overlapping Channels (NOC) and Partially Overlapping Channels (POC). It uses a new approach that optimizes the processes of tree construction, channel allocation, transmission scheduling and balancing simultaneously. Extensive simulations confirm the superiority of the proposed algorithm over the existing algorithms in wireless sensor networks.

Research on Data Acquisition System of EMU Structure Health Monitoring Based on DSP and FPGA

According to the characteristic that structural health monitoring have many monitoring sensors and monitoring signal types, with DSP and FPGA used as the core controller, a data collection system was designed. The designed system can realize multi-channel parallel data collection, flexible port configuration and good scalability, which meets the requirements of high-speed collection and processing and real-time online monitoring. The designed data acquisition system has the advantages of high performance, low cost and convenient application. In addition, the system has a wide sampling frequency range, which can adapt to a variety of sensor signal sampling requirements, is easy to install, easy to achieve network configuration, and can better meet the structural health monitoring of railway vehicles.

A Fast, Adaptive, and Energy-Efficient Data Collection Protocol in Multi-Channel-Multi-Path Wireless Sensor Networks

A wireless sensor network (WSN) consists of sensor nodes which can self-organize to relay information such as measurements to a base station. To reduce latency and increase data transmission throughput, multi-channel data collection protocols have been proposed to enable simultaneous parallel transmissions between pairs of nodes within the network. However, the existing protocols require long scheduling phase, are less dynamic to network traffic changes, and/or compromise on efficiency by relying on the back-off mechanism such as carrier sense multiple access with collision avoidance (CSMA/CA). This paper proposes a fast, adaptive, and energyefficient data collection protocol in multi-channel-multi-path WSN. The protocol consists of two major phases. The first phase is the node-channel assignment that uses the graph coloring technique to resolve the issue of node overhearing and interference. The second phase is the scheduling and packet forwarding, in which a novel three-dimensional parallel iterative matching (3DPIM) algorithm is proposed to pair up sensor nodes in different time slots so as to enable collision-free multiple simultaneous data transmissions in every time slot. Simulation results show that our proposed protocol can achieve fast and energy-efficient data collection while being adaptive to the change of network traffic in WSN.

ANALISIS SISTEM ANTRIAN PADA STASIUN PENGISIAN BAHAN BAKAR (SPBU) KOPKAR NUSA TIGA JL. SUNGGAL MEDAN

This study aims to analyze the model of queues at filling stations Kopkar Nusa Three Jl. Sunggal Medan using phase.Teknik single multi-channel data collection is done through observation and documentation. The study population was the whole vehicle wheel 2 (two) in the queuing system on weekdays applicable on Nusa Tiga Kopkar gas station Jl. Sunggal Medan. Sampling is done by nonprobability sampling Convenience sampling using primary data obtained from observation. So the data is taken against vehicles entering into the queuing system is running. From the analysis of the queuing system Kopkar Nusa Three gas station Jl. Terrain Sunggal known Poisson distributed arrival frequency, level of service and inter-arrival time is not exponentially. The level of visitor arrivals was obtained for 13 people per 8 minutes. This means that the server is quite crowded by the customer and the service provided is also capable of serving pelanggan.Waktu customers waiting in the queue (Wq) is 0871 minutes and still be within the maximum limit of the customer is willing to wait (time aspiration) that is equal to 15 minutes.

Design of multi channel data collection and processing system based on FPGA

Design of multi channel data collection and processing system based on FPGA

In-House Development of Data Collection and Signal Processing Applications for Vibration-Based Condition Monitoring

A simplified summary of in-house developed user friendly multi-channel data collection and signal processing capabilities for the vibration-based condition monitoring of plant assets in a cheaper manner is presented here.

The Design and Application of Multiplex Ground Temperature Detection System of Low Power Consumption Based on CAN Bus

This paper introduces a kind of low power ground temperature monitoring system based on CAN bus, the detection system adopts DS18B20 temperature sensor as the detecting element, under a machine with low power consumption of processor MSP430 MCU multi-channel temperature data collection, PC processors SMT32 is adopted to improve the data receiving and analysis. The ground temperature detection system has realized the low power consumption, strong anti-interference ability, low cost, has wide use value.

Development of Integrated Data Acquisition System for Logistics Packaging Material Production

Logistics transportation mostly uses foam packing material, the harmless of their production process is the current research focus. Based on National Instrument data collection cardsignal conditioning module and sensors, this paper adopted computer technology to collect data from harmless logistics packaging foam production process, and concentrates on data testing and collection methods. Based on virtual instrument technology, we build a multi-channel universal data collection system. The system exhibits high acquisition efficiency. All of the collected data are the key parameters which are closely related to harmless logistics packaging production, moreover, these parameters are relatively easy to adjust and control in actual production process.

Development of Integrated Data Acquisition System for Logistics Packaging Material Production

Logistics transportation mostly uses foam packing material, the harmless of their production process is the current research focus. Based on National Instrument data collection cardsignal conditioning module and sensors, this paper adopted computer technology to collect data from harmless logistics packaging foam production process, and concentrates on data testing and collection methods. Based on virtual instrument technology, we build a multi-channel universal data collection system. The system exhibits high acquisition efficiency. All of the collected data are the key parameters which are closely related to harmless logistics packaging production, moreover, these parameters are relatively easy to adjust and control in actual production process.

KNOWME

The use of biometric sensors for monitoring an individual’s health and related behaviors, continuously and in real time, promises to revolutionize healthcare in the near future. In an effort to better understand the complex interplay between one’s medical condition and social, environmental, and metabolic parameters, this article presents the KNOWME platform, a complete, end-to-end, body area sensing system that integrates off-the-shelf biometric sensors with a Nokia N95 mobile phone to continuously monitor the metabolic signals of a subject. With a current focus on pediatric obesity, KNOWME employs metabolic signals to monitor and evaluate physical activity. KNOWME development and in-lab deployment studies have revealed three major challenges: (1) the need for robustness to highly varying operating environments due to subject-induced variability, such as mobility or sensor placement; (2) balancing the tension between achieving high fidelity data collection and minimizing network energy consumption; and (3) accurate physical activity detection using a modest number of sensors. The KNOWME platform described herein directly addresses these three challenges. Design robustness is achieved by creating a three-tiered sensor data collection architecture. The system architecture is designed to provide robust, continuous, multichannel data collection and scales without compromising normal mobile device operation. Novel physical activity detection methods which exploit new representations of sensor signals provide accurate and efficient physical activity detection. The physical activity detection method employs personalized training phases and accounts for intersession variability. Finally, exploiting the features of the hardware implementation, a low-complexity sensor sampling algorithm is developed, resulting in significant energy savings without loss of performance.

Quasi real time monitoring system for the tomographic reconstruction of the vortex wind field

Quasi real time vortex wind field monitoring system for the tomographic reconstruction of the vortex wind velocity field was developed. The system was implemented by installing the multichannel sound transmitter and receiver pairs on both sides of the monitoring area. Sound wave travel time data were measured between the arbitrary combination of the facing transmitters and receivers. To speed up the multichannel data collection time, transmission and reception was made in parallel by sending the coded modulation signals. The demodulation correlation calculation, for the extraction of the desired data from the multiple transmission signals, was made in high speed by using GPU (Graphics Processing Unit). Test examination, using the four channel indoor system, showed the promise to complete the vortex wind velocity image production about every 1 s.

The Design of Intelligence Pipeline Leakage Detector

The Design of intelligence pipeline leakage detector is nondestructive inspection using leakage detection principle to the internal metal defects, corrosion of pipeline.The detector adopts discrete sampling, realizing 12-bits high-speed multichannel-data collection through the FPGA, amount of the data can be up to 1024 road; and store mass data through ARM at the same time, shared data will be uploaded to the computer of control center through the network, the intelligent data analysis system engineering real-time analysis and quantitative identification, judge pipeline safety operation, provides the basis for safe duty time and maintenance cycle.

Design and Realization of Management Module for Single Cell

Battery management system (BMS) is the most important part of an electronic vehicle (EV), and the management module for single cell is the most important collection part of BMS. It provides cells’ data and realizes cells management for BMS. In this paper, the design and realization of the management module for single cell is presented. It can collect real-time voltage and temperature data of cells, and carry on the necessary processing for such data, and then upload these data to the main control module in the battery management system. The design can also equalize the cells, and made their voltage remain in a standard. For the proposed design scheme, the microcontroller PIC24F16KA101 is used as the core part, and the multi-cell addressable battery stack monitor LTC6802 is utilized as the collection part. Moreover, the design of multi-channel data collection and management system, and the design of software and hardware for interface are given. The practical experimental results show that the system can realize the real-time multi-channel signal collection, offer useful data for battery management system, and equalize the cells at power on or power off situation, so as to guarantee the battery consistency.

Identification of directed interactions in networks

Multichannel data collection in the neurosciences is routine and has necessitated the development of methods to identify the direction of interactions among processes. The most widely used approach for detecting these interactions in such data is based on autoregressive models of stochastic processes, although some work has raised the possibility of serious difficulties with this approach. This article demonstrates that these difficulties are present and that they are intrinsic features of the autoregressive method. Here, we introduce a new method taking into account unobserved processes and based on coherence. Two examples of three-process networks are used to demonstrate that although coherence measures are intrinsically non-directional, a particular network configuration will be associated with a particular set of coherences. These coherences may not specify the network uniquely, but in principle will specify all network configurations consistent with their values and will also specify the relationships among the unobserved processes. Moreover, when new information becomes available, the values of the measures of association already in place do not change, but the relationships among the unobserved processes may become further resolved.

WOZ acoustic data collection for interactive TV

This paper describes a multichannel acoustic data collection recorded under the European DICIT project, during Wizard of Oz (WOZ) experiments carried out at FAU and FBK-irst laboratories. The application of interest in DICIT is a distant-talking interface for control of interactive TV working in a typical living room, with many interfering devices. The objective of the experiments was to collect a database supporting efficient development and tuning of acoustic processing algorithms for signal enhancement. In DICIT, techniques for sound source localization, multichannel acoustic echo cancellation, blind source separation, speech activity detection, speaker identification and verification as well as beamforming are combined to achieve a maximum possible reduction of the user speech impairments typical of distant-talking interfaces. The collected database permitted to simulate at preliminary stage a realistic scenario and to tailor the involved algorithms to the observed user behaviors. In order to match the project requirements, the WOZ experiments were recorded in three languages: English, German and Italian. Besides the user inputs, the database also contains non-speech related acoustic events, room impulse response measurements and video data, the latter used to compute three-dimensional positions of each subject. Sessions were manually transcribed and segmented at word level, introducing also specific labels for acoustic events.