Cyber-physical systems are systems governed by the laws of physics that are tightly controlled by computer-based algorithms and network-based sensing and actuation. Wireless communication technology is envisioned to play a primary role in conducting the information flows within such systems. A practical industrial wireless use case involving a robot manipulator control system, an integrated wireless force-torque sensor, and a remote vision-based observer is constructed and the performance of the cyber-physical system is examined. The resulting data from the experiments conducted are included in the dataset.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters. A README.doc file is included to describe the measurement apparatus.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters. A README.doc file is included to describe the measurement apparatus.

본 데이터는 한국방송통신전파진흥원에서 시행하는 국가기술자격검정 중 「통신기기기능사」의 출제기준(2023년~2024년)을 수록한 것입니다. 해당 기준은 정보통신기기(단말기기, 전송기기, 교환기기 등)의 제작, 설치, 시험, 운용 및 유지관리 직무에 필요한 기초기술을 평가하기 위한 세부항목으로 구성되어 있습니다. 필기시험 출제 기준은 전기전자공학, 전자계산기일반, 통신기기일반, 통신설비기준 등 네 과목의 이해 여부를 파악하기 위해 설정되며, 회로이론, 반도체, 디지털논리회로, 변복조 및 통신기초이론, 정보통신망 등 실무 관련 내용을 포함합니다. 실기시험 출제 기준은 공구 사용, 부품 식별, 납땜 및 배선, 측정장비 운용, 고장 수리, 설비 조립 및 동작시험 등 작업 능력의 갖추었는지 파악하기 위해 설정됩니다. 본 자료는 통신설비 분야의 기초기능인 양성을 위한 학습자료 및 시험준비 참고용으로 제공됩니다.국가기술자격검정 홈페이지(cq.or.kr)에서 자세한 내용을 확인하실 수 있습니다.

This document is a compilation of self-reported information from Federal government agencies provided in response to an Advanced Wireless Test Platforms (AWTP) Information Request by the AWTP Team co-chairs in April 2021. The AWTP Team focuses on providing a forum for interagency coordination on wireless test platforms and encourages public-private partnerships. This report complements the November 2020 Federal Mobility Group (FMG) report, Framework to Conduct 5G Testing. The FMG report compiled information about equipment (hardware and software) for existing testbeds through a survey and about visits to laboratories owned or operated by equipment vendors, mobile network operators, universities, and Federal agencies. At that time, early 2020, none of the Federal laboratories had 5G capability. The AWTP effort builds on that work but is focused on 5G and beyond infrastructure directly owned or operated by Federal Government agencies. In addition to updating the FMG study, this effort includes near-term plans, access, and data sharing elements to help facilitate coordination and collaboration.

NIST is developing metrics and test methods to benchmark the performance of robotic systems when performing manufacturing tasks. The ability to perform simple insertions is critical for robotic systems in manufacturing. A simple peg-in-hole test was designed to measure a robotic system's capability for performing these simple insertions. The dataset captures the performance metrics of a robotic system outfitted with a robotic hand and a robotic gripper to study the effect of next-generation robotic hand technology versus conventional parallel gripper technologies.

본 보유장비 목록은 국내 정보보안 업체의 제품 연구개발을 지원하기 위해 제공되는 성능시험 환경 등 이용가능 장비목록 임o 주요목차- 정보보호산업지원센터 시험환경(Test Bed) 제공- 정보보호산업지원센터 구축현황- 고성능 시험 장비 테스트랩 배치 현황(41대)

This data set is captured from a robot workcell that is performing activities representative of several manufacturing operations. The workcell contains two, 6-degree-of-freedom robot manipulators where one robot is performing material handling operations (e.g., transport parts into and out of a specific work space) while the other robot is performing a simulated precision operation (e.g., the robot touching the center of a part with a tool tip that leaves a mark on the part). This precision operation is intended to represent a precise manufacturing operation (e.g., welding, machining). The goal of this data set is to provide robot level and process level measurements of the workcell operating in nominal parameters. There are no known equipment or process degradations in the workcell. The material handling robot will perform pick and place operations, including moving simulated parts from an input area to in-process work fixtures. Once parts are placed in/on the work fixtures, the second robot will interact with the part in a specified precise manner. In this specific instance, the second robot has a pen mounted to its tool flange and is drawing the NIST logo on a surface of the part. When the precision operation is completed, the material handling robot will then move the completed part to an output. This suite of data includes process data and performance data, including timestamps. Timestamps are recorded at predefined state changes and events on the PLC and robot controllers, respectively. Each robot controller and the PLC have their own internal clocks and, due to hardware limitations, the timestamps recorded on each device are relative to their own internal clocks. All timestamp data collected on the PLC is available for real-time calculations and is recorded. The timestamps collected on the robots are only available as recorded data for post-processing and analysis. The timestamps collected on the PLC correspond to 14 part state changes throughout the processing of a part. Timestamps are recorded when PLC-monitored triggers are activated by internal processing (PLC trigger origin) or after the PLC receives an input from a robot controller (robot trigger origin). Records generated from PLC-originated triggers include parts entering the work cell, assignment of robot tasks, and parts leaving the work cell. PLC-originating triggers are activated by either internal algorithms or sensors which are monitored directly in the PLC Inputs/Outputs (I/O). Records generated from a robot-originated trigger include when a robot begins operating on a part, when the task operation is complete, and when the robot has physically cleared the fixture area and is ready for a new task assignment. Robot-originating triggers are activated by PLC I/O. Process data collected in the workcell are the variable pieces of process information. This includes the input location (single option in the initial configuration presented in this paper), the output location (single option in the initial configuration presented in this paper), the work fixture location, the part number counted from startup, and the part type (task number for drawing robot). Additional information on the context of the workcell operations and the captured data can be found in the attached files, which includes a README.txt, along with several noted publications. Disclaimer: Certain commercial entities, equipment, or materials may be identified or referenced in this data, or its supporting materials, in order to illustrate a point or concept. Such identification or reference is not intended to imply recommendation or endorsement by NIST; nor does it imply that the entities, materials, equipment or data are necessarily the best available for the purpose. The user assumes any and all risk arising from use of this dataset.