Earticle

Most of all manufacturing industries are pursuing the manufacturing process innovation through the production process automation but the printing industry still does not get out of the analog ways of the entire process compared the entire manufacturing industry. Today, many printing enterprises have the difficulties by the short for delivery, multi-item small amount printing, high quality, rise in raw material cost, drop in receiving order cost, and etc. The printing industry can get over these difficulties and issues by implementing the compact workflow line, merge with the others, automatization and networking, minimization of the repetitive operation, efficiency of the working process, optimization of the operators’ value creation, minimization of cost and materials and fast make-ready. The object of this thesis establishes the experimental data and study cases applicable in the printing industry by having high labor productivity and work in line with printing industry processes through “lean printing system”.

In our research study, we aim at optimizing multiple load in cloud, effective resource allocation and lesser response time for the job assigned. Using Hadoop on datacenter is the best and most efficient analytical service for any corporates. To provide effective and reliable performance analytical computing interface to the client, various cloud service providers host Hadoop clusters. The previous works done by many scholars were aimed at execution of workflows on Hadoop platform which also minimizes the cost of virtual machines and other computing resources. Earlier stochastic hill climbing technique was applied for single parameter and now we are working to optimize multiple parameters in the cloud data centers with proposed heuristic hill climbing. As many users try to priorities their job simultaneously in the cluster, resource optimized workflow scheduling technique should be very reliable to complete the task assigned before the deadlines and also to optimize the usage of the resources in cloud.

The size of observation data in astronomy has been increasing exponentially with the advents of wide-field optical telescopes. This means the needs of changes to the way used for large-scale astronomy data analysis. The complexity of analysis tools and the lack of extensibility of computing environments, however, lead to the difficulty and inefficiency of dealing with the huge observation data. To address this problem, this paper proposes a workflow execution system for analyzing large-scale astronomy data efficiently. The proposed system is composed of two parts: 1) a workflow execution manager and its RESTful endpoints that can automate and control data analysis tasks based on workflow templates and 2) an elastic resource manager as an underlying mechanism that can dynamically add/remove virtualized computing resources (i.e., virtual machines) according to the analysis requests. To realize our workflow execution system, we implement it on a testbed using OpenStack IaaS (Infrastructure as a Service) toolkit and HTCondor workload manager. We also exhaustively perform a broad range of experiments with different resource allocation patterns, system loads, etc. to show the effectiveness of the proposed system. The results show that the resource allocation mechanism works properly according to the number of queued and running tasks, resulting in improving resource utilization, and the workflow execution manager can handle more than 1,000 concurrent requests within a second with reasonable average response times. We finally describe a case study of data reduction system as an example application of our workflow execution system.

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

This paper proposes a policy adjuster-driven Grid workflow management system for collaborative healthcare platform, which supports collaborative heart disease diagnosis applications. To select policies according to service level agreement of users and dynamic resource status, we devised a policy adjuster to handle workflow management polices and resource management policies using policy decision scheme. We implemented this new architecture with workflow management functions based on policy quorum based resource management system for providing poincare geometrycharacterized ECG analysis and virtual heart simulation service. To evaluate our proposed system, we executed a heart disease identification application in our system and compared the performance to that of the general workflow system and PQRM system under different types of SLA.

Workflow systems have benefited the enterprise and customer in many aspects. But with the increasing complexity of the system, workflow design becomes a complicated and time-consuming process. In this paper, we model the system based on the Hierarchical Colored Petri Nets (HCPN) to avoid the complexity of the system, and the super nets of the model are abstracted as independent components to increase the flexibility, acceptability and maintainability of the system. Another important characteristic of this model is its convenience for reuse and workflow mining. The development process of the overhead traveling crane was given to demonstrate the proposed method.

Reproducibility is a critical issue in protein structure analysis, especially in drug discovery workflows. Previous methods do not consistently produce reliable results across systems and do not provide deterministic outcomes in protein analysis. This work proposes a tri-layered blockchain-inspired architecture that leverages cryptographic metadata and capabilities to validate protonation states and guarantee deterministic results and data integrity across protein structure preparation and ensure reproducible binding site analysis. The results show 100% reproducibility with a 1745.1% performance overhead with execution time still under 11 s, enhancing data security and offering a reliable solution for computational drug discovery workflows. This system enhances confidence in computational drug discovery workflows.

While generative AI's Video-to-Video technology has rapidly advanced, challenges remain in video production requiring complex camera movements and precise angle control. This study proposes a workflow that validates precise camera control by inputting reference videos created with Unity game engine into Video-to-Video generative AI. We designed five camera control missions (120⁰ rotation, vertical crane shot, compound dolly zoom, spiral orbit, and multi-angle transition) and created reference videos using Unity for each mission, then generated final videos using Runway Gen-3's Video-to-Video functionality. Focus group interviews with 5 video production experts showed that Unity references accurately conveyed camera movements across all missions (average 4.6/5 points), maintaining high accuracy (4.5 points) even in complex spiral orbit missions. Experts identified frame-level precise control, ease of iterative modification, and predictable results as key advantages, with all participants expressing intention to apply this in professional practice. This study empirically demonstrates that game engine-based reference generation is a practical solution to camera control challenges in generative AI video production.

This paper evaluates SnapCheck.io, a smart workflow platform aimed at improving the efficiency of project management in remote work environments. Through the use of AI-driven task scheduling, smart time tracking, and real-time analytics, SnapCheck.io enhances resource utilization and lowers the waiting time for task dependency. A mixed-method study at various organizations showed an improvement in performance by 18%, which was thus corroborated by the increased rate of task completion and quicker workflow execution. The results reveal that the use of intelligent automation in SnapCheck.io not only helps in streamlining the project coordination process but also promotes the ongoing productivity and collaboration of distributed teams.

4K 초고속 카메라 촬영은 해상도 및 셔터스피드 증가로 인해 빠른 피사체의 영상을 모션 블러 없이 Full HD 4배 해상도로 촬영이 가능하다. 그러나 해당 촬영은 데이터 량의 증가와 광량, 포커스 등 여러가지 제약사항이 뒤따른다. 증가된 셔터스피드로 인해 광량 부족 현상을 가져올 수 있어 조리개를 개방하여 촬영하는 경우가 많아 초점 조절에 제약이 따른다. 또한 증가된 해상도와 프레임레이트로 인해 저장 공간의 한계가 뒤따르기 때문에 촬영 기록에 제한이 따르고 있다. 따라서 본 연구는 4K 초고속 카메라(Phantom Flex 4K) 제작 사례를 통해 위와 같은 한계점들에 대해 분석하고, 이를 극복하기 위한 효율적인 워크플로우를 디자인 하였다.

4K high speed camera shooting enables shooting of fast subjects in Full HD 4 times resolution without motion blur due to increase in resolution and shutter speed. However, this shooting incurs several limitations including focus, intensity of radiation and increase in data quantity. As lack of intensity of radiation may occur due to increased shutter speed, it is shoot by opening aperture and limitation in focusing follows. In addition, there is limitation in shooting records since it has restriction in storage due to increased resolution and frame rate. In this regard, this study aims to analyze the limitations shown above through production case of 4K high speed camera (Phantom Flex 4K) and to design effective workflow to overcome this.

사회적 취약계층을 위한 보완 대체 기술적 연구가 활발하게 연구되어지고 있다. 특히, 의사소통 분야에서는 비장애인과 장애인이 자연스럽게 의사를 주고받기 위해 여러 가지 방법과 장치가 나왔지만, 아직까지 비장애인과 같이 완벽한 의사소통을 하기에는 기술적인 보완과 지속적인 연구가 필요한 시점이다. 따라서, 본 논문은 중증장애 및 의사소통에 어려움을 가지고 있는 장애인들이 보다 쉽고 빠르게 의사를 전달하여, 생활함에 있어 도움을 주는데 목적이 있다. 중증장애인의 의사소통을 원활하게 이루어 질수 있도록 도와주는 Smart Workflow AAC(Augmentative and Alternative Communication) 기능과 장애인의 상황별 편리하게 서비스를 제공해 주는 IR센서 기반의 상황 인지 기능을 개발하였다. 제한된 시스템은 실험을 통하여, 개개인의 장애 상황별 스케줄을 관리 및 장애인의 의사소통에 있어서 보다 직관적으로 전달하는데 효율적인 것이 증명 되었다.

This study is about Augmentative and Alternative Communication as handicapped people actively pursue their life. Especially, although there are many ways and devices for communicating the same as a normal person, descriptive supplementation and sustaining studies are needed for perfect communication as a normal person. The purpose of the paper is to help handicapped people who have hardships about communication. This study develops the function of Smart Workflow AAC(Augmentative and Alternative Communication) and IR sensor_based context aware function that can offer services convenient to classified handicapped people for helping them to communicate effectively. This system from an experimental basis has improved efficiency to communicate more harmoniously within individual handicapped situations to schedule management and communication of handicapped people.

사회의 발달에 따라 정보의 양이 폭발적으로 증가하게 되어 이를 효율적으로 유지, 보관하기 위해 디지털 아카이브의 필요성이 대두되었다. 현재 아카이브의 표준으로는 OAIS(Open Archival Information System) 참조 모형이 있다. 이 모형은 장기보존에만 관심이 있고, 주로 포괄적인 콘텐츠에만 신경을 쓰고 있다. 본 논문에서는 이미지 자료를 대상으로 장기보존만이 아닌 효율적인 활용을 위한 디지털 아카이브 워크플로우와 메타데이터에 대한 모델을 제시하고자 한다. 이미지 자료 디지털 아카이브 구축을 위해서는 OAIS 참조 모형을 통해 서지 정보와 미디어 형태의 메타데이터 표준이 추가되어야 한다는 필요성이 제기된다. 이미지 정보의 효율적인 디지털 아카이빙 및 활용을 위한 클래스 기반 다단계 메타데이터 모델 관리를 제안한다.

As the amount of information increases explosively with the development of society, the need for a digital archive has emerged in order to maintain information efficiently. There is a current standard of digital archive is OAIS(Open Archival Information System) reference model. OAIS reference model is mainly interested in long-term preservation and concerned about comprehensive content. This paper propose digital archive workflow that is used not only for long-term preservation but also for the efficient utilization with the image data. The OAIS reference model and the metadata standard should be added surge information to build a digital archive. Therefore we propose metadata model should be managed based on multi-level classes not only for effective digital image archive but also for utilization.