Grasping and Assembling with Modular Robots

A wide variety of problems, from manufacturing to disaster response and space exploration, can benefit from robotic systems that can firmly grasp objects or assemble various structures, particularly in difficult, dangerous environments. In this thesis, we study the two problems, robotic grasping and assembly, with a modular robotic approach that can facilitate the problems with versatility and robustness. First, this thesis develops a theoretical framework for grasping objects with customized effectors that have curved contact surfaces, with applications to modular robots. We present a collection of grasps and cages that can effectively restrain the mobility of a wide range of objects including polyhedra. Each of the grasps or cages is formed by at most three effectors. A stable grasp is obtained by simple motion planning and control. Based on the theory, we create a robotic system comprised of a modular manipulator equipped with customized end-effectors and a software suite for planning and control of the manipulator. Second, this thesis presents efficient assembly planning algorithms for constructing planar target structures collectively with a collection of homogeneous mobile modular robots. The algorithms are provably correct and address arbitrary target structures that may include internal holes. The resultant assembly plan supports parallel assembly and guarantees easy accessibility in the sense that a robot does not have to pass through a narrow gap while approaching its target position. Finally, we extend the algorithms to address various symmetric patterns formed by a collection of congruent rectangles on the plane. The basic ideas in this thesis have broad applications to manufacturing (restraint), humanitarian missions (forming airfields on the high seas), and service robotics (grasping and manipulation)

CMB Spectral μ\mu-Distortion of Multiple Inflation Scenario

In multiple inflation scenario having two inflations with an intermediate matter-dominated phase, the power spectrum is estimated to be enhanced on scales smaller than the horizon size at the beginning of the second inflation, $k > k_{\rm b}$. We require $k_{\rm b} > 10 {\rm Mpc}^{-1}$ to make sure that the enhanced power spectrum is consistent with large scale observation of cosmic microwave background (CMB). We consider the CMB spectral distortions generated by the dissipation of acoustic waves to constrain the power spectrum. The $\mu$-distortion value can be $10$ times larger than the expectation of the standard $\Lambda$CDM model ($\mu_{\Lambda\mathrm{CDM}} \simeq 2 \times 10^{-8}$) for $k_{\rm b} \lesssim 10^3 {\rm Mpc}^{-1}$, while the $y$-distortion is hardly affected by the enhancement of the power spectrum.Comment: 16 pages, 5 figure

Minimum Word Error Rate Training for Speech Separation

Master's thesis in Computer scienceThe cocktail party problem, also known as a single-channel multi-talker problem, is a significant challenge to enhance the performance of automatic speech recognition (ASR) systems. Most existing speech separation model only concerns the signal-level performance, i.e., source-to-distortion ratio (SDR), via their cost/loss function, not a transcription-level performance. However, transcription-level measurement, such as word error rate (WER) is the ultimate measurement that can be used in the performance of ASR. Therefore we propose a new loss function that can directly consider both signal and transcription level performance with integrating both speech separation and speech recognition system. Moreover, we suggest the generalized integration architecture that can be applied to any combination of speech recognition/separation system regardless of their system environment. In this thesis, first, we review the techniques from the primary signal processing knowledge to deep learning techniques and introduce the detailed target and challenge in speech separation problem. Moreover, we analyze the several famous speech separation models derived from a deep learning approach. Then we introduce the new loss function with our detailed system architecture, including the step-by-step process from pre-processing to evaluation. We improve the performance of the existing model using our training approach. Our solution enhances average SDR from 0.10dB to 4.09dB and average WER from 92.7% to 55.7% using LibriSpeech dataset

Minimum Word Error Rate Training for Speech Separation

The cocktail party problem, also known as a single-channel multi-talker problem, is a significant challenge to enhance the performance of automatic speech recognition (ASR) systems. Most existing speech separation model only concerns the signal-level performance, i.e., source-to-distortion ratio (SDR), via their cost/loss function, not a transcription-level performance. However, transcription-level measurement, such as word error rate (WER) is the ultimate measurement that can be used in the performance of ASR. Therefore we propose a new loss function that can directly consider both signal and transcription level performance with integrating both speech separation and speech recognition system. Moreover, we suggest the generalized integration architecture that can be applied to any combination of speech recognition/separation system regardless of their system environment. In this thesis, first, we review the techniques from the primary signal processing knowledge to deep learning techniques and introduce the detailed target and challenge in speech separation problem. Moreover, we analyze the several famous speech separation models derived from a deep learning approach. Then we introduce the new loss function with our detailed system architecture, including the step-by-step process from pre-processing to evaluation. We improve the performance of the existing model using our training approach. Our solution enhances average SDR from 0.10dB to 4.09dB and average WER from 92.7% to 55.7% using LibriSpeech dataset

An aggravated return-to-work case of organic solvent induced chronic toxic encephalopathy

Abstract Background Organic solvent-induced chronic toxic encephalopathy (CTE) is known as a non-progressive disorder that does not progress after diagnosis. The authors present a case those symptoms worsened after continued exposure to organic solvent after returning to work. Because such a case has not been reported in South Korea to the best of our knowledge, we intend to report this case along with literature review. Case presentation A 59-year-old man, who performed painting job at a large shipyard for 20 years, was receiving hospital treatment mainly for depression. During the inpatient treatment, severe cognitive impairment was identified, and he visited the occupational and environmental medicine outpatient clinic for assessing work relatedness. In 1984, at the age of 27, he began performing touch-up and spray painting as a shipyard painter. Before that he had not been exposure to any neurotoxic substances. In 2001, at the age of 44, after 15 years of exposure to mixed solvents including toluene, xylene and others, he was diagnosed with CTE International Solvent Workshop (ISW) type 2A. After 7 years of sick leave, he returned to work in 2006. And he repeated return-to-work and sick leave in the same job due to worsening of depressive symptoms. He had worked four times (2006–2010, 2011–2011, 2011–2011, 2016–2017) for a total of 5 years as a shipyard painter after first compensation. During the return-to-work period, the mean values of the mixed solvent index ranged from 0.57 to 2.15, and except for a one semiannual period, all mean values were above the standard value of 1. We excluded other diseases that can cause cognitive impairment like central nervous system diseases, brain injury, psychological diseases and metabolic diseases with physical examinations, laboratory tests, and brain image analysis. And finally, throughout neuropsychological tests, an overall deterioration in cognitive function was identified compared to 2002, and the deterioration types was similar to that often shown in the case of CTE; thus a diagnosis of CTE (ISW) type 3 was made. Conclusion This case is showing that CTE can go on with continued exposure to mixed solvents. Appropriate “fitness to work” should be taken to prevent disease deterioration especially for the sick leave workers

Effects of surface defects on rolling contact fatigue of rail

AbstractRolling contact fatigue damages on the surface of rail such as head check, squats are one of growing problems. Squats are originated from the white etching layer (WEL) or the dent on rail surface and accompanied with dark spots including cracks. Another form of rail surface damage, known as “Ballast imprint” has become apparent. This form of damage is associated with ballast particles becoming trapped between the wheel and the surface of rail. In this study, we have investigated whether the ballast imprint is an initiator of head check type cracks using the twin disc test and Finite element analysis. The tests were conducted using specimens with artificial defects. FE analysis were used to investigate stresses and strains in subsurface of defects. The test results show that cracks initiate and propagate in dents larger than a certain size

Modular Reconfigurable Robotics

This article reviews the current state of the art in the development of modular reconfigurable robot (MRR) systems and suggests promising future research directions. A wide variety of MRR systems have been presented to date, and these robots promise to be versatile, robust, and low cost compared with other conventional robot systems. MRR systems thus have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. We begin by introducing the taxonomy of MRRs based on their hardware architecture. We then examine recent progress in the hardware and the software technologies for MRRs, along with remaining technical issues. We conclude with a discussion of open challenges and future research directions

A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus

Among various consensus algorithms, the Byzantine Fault Tolerance (BFT)-based consensus algorithms are broadly used for private blockchain. However, as BFT-based consensus algorithms are structured for all participants to take part in a consensus process, a scalability issue becomes more noticeable. In this approach, we introduce a consensus coordinator to execute a conditionally BFT-based consensus algorithm by classifying transactions. Transactions are divided into equal and unequal transactions. Moreover, unequal transactions are divided again and classified as common and trouble transactions. After that, a consensus algorithm is only executed for trouble transactions, and BFT-based consensus algorithms can achieve scalability. For evaluating our approach, we carried out three experiments in response to three research questions. By applying our approach to PBFT, we obtained 4.75 times better performance than using only PBFT. In the other experiment, we applied our approach to IBFT of Hyperledger Besu, and our result shows a 61.81% performance improvement. In all experiments depending on the change of the number of blockchain nodes, we obtained the better performance than original BFT-based consensus algorithms; thus, we can conclude that our approach improved the scalability of original BFT-based consensus algorithms. We also showed a correlation between performance and trouble transactions associated with transaction issue intervals and the number of blockchain nodes

Review of carcinogenicity of hexavalent chrome and proposal of revising approval standards for an occupational cancers in Korea

Abstract Background The objective of this study is to suggest revised recognition standards for occupational disease due to chromium (VI) by reflecting recent domestic and international research works and considering domestic exposure status with respect to target organs, exposure period, and cumulative exposure dose in relation to the chromium (VI)-induced occupational disease compensation. Methods In this study, the reports published by major international institutions such as World Health Organization (WHO) International Agency for Research on Cancer (IARC) (2012), Occupational Safety and Health Administration (OSHA) (2006), National Institute for Occupational Safety and Health (NIOSH) (2013), American Conference of Governmental Industrial Hygienists (ACGIH) (2004), National Toxicology Program (NTP) (2014), and Agency for Toxic Substances and Disease Registry (ASTDR) (2012) were reviewed and the recent research works searched by PubMed were summarized. Results Considering the recent research works and the domestic situation, only lung cancer is conserved in the legislative bill in relation to chromium (VI), and the exposure period is not included in the bill. Nasal and paranasal sinus cancer was excluded from the list of cancers that are compensated as the chromium (VI)- induced occupational disease, while lung cancer remains in the list. In the view of legislative unity, considering the fact that only the cancers having sufficient evidence are included in the conventional list of cancers compensated as occupational disease, nasal and paranasal sinus cancer having limited evidence were excluded from the list. The exposure period was also removed from the legislative bill due to the insufficient evidence. Recent advices in connection with cumulative exposure dose were proposed, and other considerable points were provided with respect to individual occupational relevance. Conclusions It is suggested that the current recognition standard which is “Lung cancer or nasal and paranasal sinus cancer caused by exposure to chromium (VI) or compounds thereof (exposure for two years or longer), or nickel compounds” should be changed to “Lung cancer caused by exposure to chromium (VI) or compounds thereof, and lung cancer or nasal and paranasal sinus cancer caused by exposure to nickel compounds”