A Systems Approach for Selection Between Manual and Automated Work Zones Within Assembly Lines

Manufacturing firms are continuously looking forward to improve and optimize their processes to meet the requirements of mass production and product customization. In order to meet these demands, the operations on the assembly line need to be allocated with the right level of automation, such that neither the human nor the machine is underutilized With such an emphasis being put on assembly operations within manufacturing enterprises, there is a need for a systematic procedure that helps in identifying appropriate levels of automation (LoA) within different resolutions, such as at the workstation, and the band scales. Based on a literature review, it was seen that the research done within the area of LoA is not abundant, and the few methodologies that discuss about this aspect have their own benefits and limitations. The main aim of this thesis research is to develop a systematic methodology/approach that can help determine the appropriate at a systems level, by looking at various factors such as production volume, production flow, the no. of variants and other factors. To arrive at this, a set of requirements are defined that can be used to judge the most suitable method from the existing literature. The most suitable method would be a method that satisfies all the requirements and helps in determining the appropriate LoA at workstation and band scales. Two methods: 1) B&D method and 2) Dynamo method partially satisfy most of the requirements and are combined together in order to form a new integrated method that can help in determining the appropriate levels of automation to be applied at workstation and band scales. Both the methods are validated based on 4 individual case studies performed at 2 different manufacturing firms. Based on the results obtained both the methods are useful at the workstation level but fail to determine the appropriate LoA at the band level. The integrated method is then applied to the operations at one of the manufacturing firms, to suggest possible improvements within the levels of automation currently being implemented at the firm

CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called `CoaCor'), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201

Embedded Cryptography: An Analysis and Evaluation of Performance and Code Optimization Techniques for Encryption and Decryption in Embedded Systems

It is clear that Cryptography is computationally intensive. It is also known that embedded systems have slow clock rates and less memory. The idea for this thesis was to study the possibilities for analysis of cryptography on embedded systems. The basic approach was the implementation of cryptographic algorithms on high-end, state-of-the-art, DSP chips in order to study the various parameters that optimize the performance of the chip while keeping the overhead of encryption and decryption to a minimum. Embedded systems are very resource sensitive. An embedded system is composed of different components, which are implemented in both hardware and software. Therefore, hardware-software co-synthesis is a crucial factor affecting the performance of embedded systems. Encryption algorithms are generally classified as data-dominated systems rather than ubiquitous control-dominated systems. Data-dominated systems have a high degree of parallelism. Embedded systems populate the new generation gadgets such as cell phones and Smartcards where the encryption algorithms are obviously an integral part of the system. Due to the proliferation of embedded systems in all the current areas, there is a need for the systematic study of encryption techniques from the embedded systems point of view. This thesis explored the different ways encryption algorithms can be made to run faster with much less memory. Some of the issues investigated were overlapped scheduling techniques for high-level synthesis, structural partitioning, real-time issues, reusability and functionality, random number and unique key generators, seamless integration of cryptographic code with other applications and architecture specific optimization techniques

Methods for Selecting Level of Automation: A Critical Comparison of Approaches and Integrated Proposal

International audienceThe purpose of this paper is to first review and then propose methods for determining the appropriate levels of automation for assembly operations. Based on a literature review in decision making methods, an evaluation against method requirements is made. The analysis shows that no single method fulfills all the defined requirements, yet two methods are identified that jointly address all the requirements: Boothroyd’s and Dewhurst (B&D) method and the Dynamo method. B&D and Dynamo methods are then combined into a new method, exploiting elements of each. An additional step for graphical modeling of the assembly processes using the Assembly Sequence Modeling Language (ASML) is integrated to facilitate alternative exploration. This newly proposed method is discussed and revealed promising in the field of assembly systems Level of Automation (LoA) measurement and improvement

Methods for Selecting Level of Automation: A Critical Comparison of Approaches and Integrated Proposal

International audienceThe purpose of this paper is to first review and then propose methods for determining the appropriate levels of automation for assembly operations. Based on a literature review in decision making methods, an evaluation against method requirements is made. The analysis shows that no single method fulfills all the defined requirements, yet two methods are identified that jointly address all the requirements: Boothroyd’s and Dewhurst (B&D) method and the Dynamo method. B&D and Dynamo methods are then combined into a new method, exploiting elements of each. An additional step for graphical modeling of the assembly processes using the Assembly Sequence Modeling Language (ASML) is integrated to facilitate alternative exploration. This newly proposed method is discussed and revealed promising in the field of assembly systems Level of Automation (LoA) measurement and improvement

Management of proximal tibial stress fracture associated with advanced knee osteoarthritis: A systematic review

Purpose: Tibial stress fracture associated with knee osteoarthritis is an unusual and difficult clinical scenario. There is no clear existing treatment guideline for this uncommon clinical disease. The aim of this study is to review the impact of various treatment options for patients with advanced knee osteoarthritis associated with proximal tibial stress fracture. Methods: The study was performed using the databases of PubMed and Scopus. Methodological index for non-randomized studies score was used to evaluate the included studies’ bias. The concluded data included the treatment approach, reported outcome measure, and time to fracture union. The literature search was started in December 2021 and accomplished in January 2022. A narrative description of the different methods and comparison of their results were done. Results: Out of total assessed 69 studies, 9 studies were included in our review. The commonest treatment approach used was total knee arthroplasty by long tibial stem extension. The mean preoperative knee society score and knee functional score were 30.62 and 23.17, respectively. The mean postoperative knee society knee score was 86.87, while the functional score was 83.52. The average reported time to achieve fracture union was 4 months (a range of 2.07 – 5.50 months). Conclusion: The optimal clinical outcome for treating either acute or mobile tibial stress fracture in patients with advanced knee osteoarthritis can be achieved with long stem total knee arthroplasty. However, due to heterogeneity of data, comparison of different treatment options for chronic proximal tibial stress fracture mal-union/non-union coexisting with knee osteoarthritic and such inferences need to be judged cautiously

A System-Wide Debugging Assistant Powered by Natural Language Processing

© 2019 ACM. Despite advances in debugging tools, systems debugging today remains largely manual. A developer typically follows an iterative and time-consuming process to move from a reported bug to a bug fix. This is because developers are still responsible for making sense of system-wide semantics, bridging together outputs and features from existing debugging tools, and extracting information from many diverse data sources (e.g., bug reports, source code, comments, documentation, and execution traces). We believe that the latest statistical natural language processing (NLP) techniques can help automatically analyze these data sources and significantly improve the systems debugging experience. We present early results to highlight the promise of NLP-powered debugging, and discuss systems and learning challenges that must be overcome to realize this vision