• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.171-176
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• 2020

This paper presents an implementation of a gesture recognition platform that can be used in a factory workplaces. The platform consists of signages that display worker's job orders and a control center that is used to manage work orders for factory workers. Each worker does not need to bring work order documents and can browse the assigned work orders on the signage at his/her workplace. The contents of signage can be controlled by worker's hand and arm gestures. Gestures are extracted from body movement tracked by 3D depth camera and converted to the commandsthat control displayed content of the signage. Using the control center, the factory manager can assign tasks to each worker, upload work order documents to the system, and see each worker's progress. The implementation has been applied experimentally to a machining factory workplace. This flatform provides convenience for factory workers when they are working at workplaces, improves security of techincal documents, but can also be used to build smart factories.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.246-252
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• 2020

Facial recognition is a biometric technology that is used in various fields such as user authentication and identification of human characteristics. Face recognition applications are practically used in various fields, but very few applications have been developed to improve the factory work environment. We implemented applications that uses face recognition to identify a specific employee in a factory .work environment and provide customized information for each employee. Factory workers need documents describing the work in order to do their assigned work. Factory managers can use our application to register documents needed for each worker, and workers can view the documents assigned to them. Each worker is identified using face recognition, and by tracking the worker's face during work, it is possible to know that the worker is in the workplace. In addition, as a mobile app for workers is provided, workers can view the contents using a tablet, and we have defined a simple communication protocol to exchange information between our applications. We demonstrated the applications in a factory work environment and found several improvements were required for practical use. We expect these results can be used to improve factory work environments.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.4 no.1
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• pp.33-42
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• 1994

This study was carried out to assess worker exposure to vinyl chloride monomer (VCM) and to present control measures in the factories processing and producing polyvinyl chloride (PVC) resin. The conclusion remarks are as follows. Only two personal samples in the factory ("E") processing polyvinyl chloride resin were analysed to be 27.6 ppm and 12.6 ppm, respectively. But, these concentration exceed 1 ppm, Permissible Exposure Limits (PEL) of OSHA. So, worker's exposure to VCM at "E" factory should be reevaluated. In "A", "B" and "C" factory producing polyvinyl chloride resin, the average worker's exposures to VCM were 0.12 ppm, 0.86 ppm and 1.23 ppm, respectivery. Worker exposure to VCM at distillation and dry process was higer than other processes at "A" factory. The average exposure concentration of worker at polymerization process of "B" and "C" factory was 1.23 ppm, and 1.46 ppm respcetively. These concentration exceed 1 ppm, Permissible Exposure Limits of OSHA. Control room of "B" and "C" factory had 0.91 ppm and 0.65 ppm of worker's exposure concentration respectively. "A" factory was evaluated to be "acceptable", but "B" and "C" factories were evaluated to be "not acceptable", by the workplace exposure assessment program of AIHA. Process other than bagging and control room of "A" factory was evaluated to "not acceptable". Immediate correction measures for preventing workers from exposure to VCM should be performed in the factories or process that were evaluated to be "not acceptable". After these control measures are taken, worker exposure to VCM must be reevaluated through personal air monitoring. Control measures presented by this study are complete sealing of connecting pipe lines, flanging, packing, bolting and nutting. Periodic leak test for leak parts is also required. And positive pressure facility should be constructed at control room of "B" and "C" factory. Fresh air through cleaner such as HEPA filter should be supplied to control room. In addition to these control measures, periodic personal monitoring for evaluating worker exposure to VCM should be performed.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.33-40
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• 2013

Purpose: The goal of this study was to build an accurate digital factory that evaluates the performance of a factory using computer simulation. To achieve this goal, we evaluated the effect of worker-related variables on production in a simulation model using comparative analysis of two cases. Methods: The overall work process and worker-related variables were determined and used to build a simulation model. Siemens PLM Software's Plant Simulation was used to build a simulation model. Also, two simulation models were built, where the only difference was the use of the worker-related variable, and the total daily production analyzed and compared in terms of the individual process. Additionally, worker efficiency was evaluated based on worker analysis. Results: When the daily production of the two models were compared, a 0.16% error rate was observed for the model where the worker-related variables were applied and error rate was approximately 5.35% for the model where the worker-related variables were not applied. In addition, the production in the individual processes showed lower error rate in the model that included the worker-related variables than the model where the worker-related variables were not used. Also, among the total of 22 workers, only three workers satisfied the IFRS (International Financial Reporting Standards) suggested worker capacity rate (90%). Conclusions: In the daily total production and individual process production, the model that included the worker-related variables produced results that were closer to the real production values. This result indicates the importance of worker elements as input variables, in regards to building accurate simulation models. Also, as suggested in this study, the model that included the worker-related variables can be utilized to analyze in more detail actual production. The results from this study are expected to be utilized to improve the work process and worker efficiency.

• Asian Journal of Atmospheric Environment
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• v.11 no.2
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• pp.122-130
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• 2017

The main purpose of the study was to clarify the properties of individual particles collected at each behavior space of a factory worker. The samplings of size-segregated ($PM_{2.1-1.1}$ and $PM_{4.7-3.3}$) indoor particles were conducted at three different behavior spaces of a factory worker who is engaged in an auto parts manufacturing plant (i.e., his home, his work place in factory, and his favorite restaurant). Elemental specification (i.e., relative elemental content and distribution in and/or on individual particles) was performed by a micro-PIXE system. Every element detected from the coarse particulate matters of home was classified into three groups, i.e., a group of high net-counts (Na, Al, and Si), a group of intermediate net-counts (Mg, S, Cl, K, and Ca), and a group of minor trace elements (P, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb). The results of EF for $PM_{4.7-3.3}$ in home indicated that several heavy metals were generated from the sources within the house itself. An exceptional feature shown in the individual particles in workplace is that Cr, Mn, and Co were clearly detected in both fine and coarse particles. Cluster analysis suggested that the individual coarse particles ($PM_{4.7-3.3}$) collected at the indoor of factory were chemically heterogeneous and they modified with sea-salt, mineral, and artificially derived elements. The principal components in individual coarse particles collected at restaurant were sea-salt and mineral without mixing with harmful trace elements like chromium and manganese. Compared to the indoor fine particles of home and restaurant, many elements, especially, Cl, Na, Cr, Mn, Pb, and Zn showed overwhelmingly high net-counts in those of factory.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.143-150
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• 2020

Recently, many companies are interested in smart factory services. Because various smart factory services are provided by the combination of mobile devices, cloud computing, and IoT services. However, many workers turn away from these systems because most of them are not implemented from the worker's point of view. To solve this, we implemented a development tool that allows field workers to produce their own services so that workers can easily create smart factory services. Manufacturing data is collected in real time from sensors which are connected to manufacturing facilities and stored within smart factory platforms. Implemented development tools can produce services such as monitoring, processing, analysis, and control of manufacturing data in drag-and-drop. The implemented system is effective for small manufacturing companies because of their environment: making various services quickly according to the company's purpose. In addition, it is assumed that this also will help workers' improve operation skills on running smart factories and fostering smart factory capable personnel.

• YAKHAK HOEJI
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• v.37 no.5
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• pp.458-462
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• 1993

Blood toluene concentrations of thirty nine Korean toluene-exposed workers in shoes making factory were checked by headspace-gas chromatographic analysis. Air toluene concentrations in each worker's working region also checked by personal sampler during workshift and analyzed by gas chromatography. The range of blood toluene concentration was 0.15-0.84mg/L. The range of toluene concentration of each worker's working area was 8.46-189.9ppm. The correlation between blood and air concentration of toluene was 0.824.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.73-76
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• 2020

본 논문은 생산 현장에서 필요로 하는 다양한 스마트팩토리 서비스를 현장 근로자가 직접 기획, 설계, 구현 및 적용 가능한 서비스 플랫폼을 제안한다. 이를 위하여 오픈 하드웨어 개발 도구 등을 활용한 IoT 기반 제조데이터 수집과 이를 활용하여 서비스 화면을 구성할 수 있는 개발도구를 설계하고 구현하였으며, 구현된 프로그램으로부터 제조데이터 기반의 다양한 현장 서비스를 근로자가 직접 만들고 배포할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.109-114
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• 2024

In this paper, we design and implement a worker motion 3D visualization module based on human sensors. The three key modules that make up this system are Human Sensor Implementation, Data Set Creation, and Visualization. Human Sensor Implementation provides the functions of setting and installing the human sensor locations and collecting worker motion data through the human sensors. Data Set Creation offers functions for converting and storing motion data, creating near real-time worker motion data sets, and processing and managing sensor and motion data sets. Visualization provides functions for visualizing the worker's 3D model, evaluating motions, calculating loads, and managing large-scale data. In worker 3D model visualization, motion data sets (Skeleton & Position) are synchronized and mapped to the worker's 3D model, and the worker's 3D model motion animation is visualized by combining the worker's 3D model with analysis results. The human sensor-based worker motion 3D visualization module designed and implemented in this paper can be widely utilized as a foundational technology in the smart factory field in the future.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.89-97
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• 2021

Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.