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ATSNet: An Attention-Based Tumor Segmentation Network 基于注意力的肿瘤分割网络
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703157
Eashan Sapre, Abhishek Chakravarthi, S. Bhanot
{"title":"ATSNet: An Attention-Based Tumor Segmentation Network","authors":"Eashan Sapre, Abhishek Chakravarthi, S. Bhanot","doi":"10.1109/ICC54714.2021.9703157","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703157","url":null,"abstract":"Science and technology has had a huge impact in the field of medicine leading to more accurate and preventive diagnosis, and treatment. Detecting brain tumors in early stages is essential for timely treatment of patients. Automatic segmentation of brain tumors is a challenging task as tumors vary in shapes and size. In this paper, we propose a fully automatic novel deep learning architecture for brain tumor segmentation named ATSNet. The network provides an end-to-end solution for feature extraction and brain tumor segmentation on Magnetic Resonance Images. Our proposed model uses an encoder-decoder architecture, employing residual modules for tackling gradient dispersion and uses skip connections for better feature map synthesis. The network utilizes attention gates (AG) to tackle the variability of brain tumors. Performance metrics such as dice score, precision, recall and intersection-over-union (IoU) have been used to evaluate and benchmark our model against those reported in literature. We have evaluated our model using the k-fold cross-validation approach. Our analysis also includes an ablation study on our model to identify important parts of the architecture by their effect on performance for optimizing the model.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126990926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Reinforcement Learning for Web Crawling 网络爬行的深度强化学习
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703160
Konstantin Avrachenkov, V. Borkar, K. Patil
{"title":"Deep Reinforcement Learning for Web Crawling","authors":"Konstantin Avrachenkov, V. Borkar, K. Patil","doi":"10.1109/ICC54714.2021.9703160","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703160","url":null,"abstract":"A search engine uses a web crawler to crawl the pages from the world wide web (WWW) and aims to maintain its local cache as fresh as possible. Unfortunately, the rates at which different pages change in WWW are highly nonuniform and also, unknown in many real-life scenarios. In addition, the finite available bandwidth and possible server restrictions on crawling frequency make it very difficult for the crawler to find the optimal scheduling policy that maximises the freshness of the local cache. We model this problem in a multi-armed restless bandits framework, where each arm represents a web page or an aggregate of statistically identical web pages. The objective is to find the scheduling policy that gives the exact indices of the pages to be crawled at a particular instance. We provide an online learning scheme using deep reinforcement learning (DRL) framework which learns the unknown page change dynamics on the fly along with the optimal crawling policy. Finally, we run numerical simulations to compare our approach with state-of-the-art algorithms such as static optimisation and Thompson sampling. We observe better performance for DRL.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"45 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126350431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Attitude Stabilization of a Rigid Body with Communication Time Delay 具有通信时滞的刚体姿态稳定
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703129
Manmohan Sharma, I. Kar
{"title":"Attitude Stabilization of a Rigid Body with Communication Time Delay","authors":"Manmohan Sharma, I. Kar","doi":"10.1109/ICC54714.2021.9703129","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703129","url":null,"abstract":"A predictor augmented with a geometric controller is proposed to stabilize the attitude of a rigid body with both states as well as input time delay, possibly encountered while operating over a network. The predictor, as well as the controller, are proposed on SO(3) to remove the singularities and ambiguities associated with local representation. The predictor predicts the future states of the rigid body and the output of the predictor is used as input to the geometric controller. It is proved with the help of the Lyapunov Razumikhin theorem that the predictor is asymptotically stable. The controller is also shown to be asymptotically stable. Numerical simulation, as well as comparison results, are given to show the superiority of the proposed approach.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125752038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Chaos Synchronization, Anti-Synchronization, and Parameter Estimation in a Chaotic System with Coexisting Hidden Attractors 隐吸引子共存混沌系统的混沌同步、反同步及参数估计
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703188
L. Moysis, Meenakshi Tripathi, M. Gupta, C. Volos
{"title":"Chaos Synchronization, Anti-Synchronization, and Parameter Estimation in a Chaotic System with Coexisting Hidden Attractors","authors":"L. Moysis, Meenakshi Tripathi, M. Gupta, C. Volos","doi":"10.1109/ICC54714.2021.9703188","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703188","url":null,"abstract":"This work considers the problem of chaos synchronization and parameter estimation, for the special case where the unknown parameters are linearly injected into the system. This case is of particular interest because scalar parameters often appear in systems with hidden attractors. For this type of system, state and parameter estimation can be achieved simultaneously by appropriately rewriting the system into descriptor form, and designing an observer that estimates the augmented system's state. The design is adapted to achieve anti-synchronization as well, and a unified switching observer is proposed that can achieve both synchronization and anti-synchronization, based on the values of a switching signal. The design is showcased through an example of a modified chaotic system with hidden, coexisting attractors.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125871840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Regret-Minimization in Risk-Averse Bandits 风险规避型强盗的后悔最小化
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703134
Shubhada Agrawal, S. Juneja, Wouter M. Koolen
{"title":"Regret-Minimization in Risk-Averse Bandits","authors":"Shubhada Agrawal, S. Juneja, Wouter M. Koolen","doi":"10.1109/ICC54714.2021.9703134","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703134","url":null,"abstract":"Classical regret minimization in a bandit frame-work involves a number of probability distributions or arms that are not known to the learner but that can be sampled from or pulled. The learner's aim is to sequentially pull these arms so as to maximize the number of times the best arm is pulled, or equivalently, minimize the regret associated with the sub-optimal pulls. Best is classically defined as the arm with the largest mean. Lower bounds on expected regret are well known, and lately, in great generality, efficient algorithms that match the lower bounds have been developed. In this paper we extend this methodology to a more general risk-reward set-up where the best arm corresponds to the one with the lowest average loss (negative of reward), with a multiple of Conditional-Value-at-Risk $(mathbf{CVaR})$ of the loss distribution added to it. $(mathbf{CVaR})$ is a popular tail risk measure. The settings where risk becomes an important consideration, typically involve heavy-tailed distributions. Unlike in most of the previous literature, we allow for all the distributions with a known uniform bound on the moment of order $(1+epsilon)$, allowing for heavy-tailed bandits. We extend the lower bound of the classical regret minimization setup to this setting and develop an index-based algorithm. Like the popular KL-UCB algorithm for the mean setting, our index is derived from the proposed lower bound, and is based on the empirical likelihood principle. We also propose anytime-valid confidence intervals for the mean-CVaR trade-off metric. En route, we develop concentration inequalities, which may be of independent interest.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126070790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Decentralized Adaptive Coverage Control of Heterogeneous Mobile Robots 异构移动机器人的分散自适应覆盖控制
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703178
Nijil George, Dwaipayan Mukherjee
{"title":"Decentralized Adaptive Coverage Control of Heterogeneous Mobile Robots","authors":"Nijil George, Dwaipayan Mukherjee","doi":"10.1109/ICC54714.2021.9703178","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703178","url":null,"abstract":"This paper proposes an algorithm for decentralized adaptive coverage control of a heterogeneous group of agents in a convex environment. The kinematics of each agent can be described by either a single integrator, a double integrator, or a unicycle. The function defining the relative importance of points in the environment is not known to the agents, and an adaptation law is used by the agents to learn about the relative importance over time. A Lyapunov based stability and convergence proof is provided for the proposed algorithm and it is verified through several relevant simulations.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123486118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Distance-Constrained Formation Control of Multi-Agent Systems Using Asymmetric Barrier Lyapunov Function 基于非对称屏障Lyapunov函数的多智能体系统距离约束编队控制
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703147
Shubham Singh, Anoop Jain
{"title":"Distance-Constrained Formation Control of Multi-Agent Systems Using Asymmetric Barrier Lyapunov Function","authors":"Shubham Singh, Anoop Jain","doi":"10.1109/ICC54714.2021.9703147","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703147","url":null,"abstract":"This paper studies the formation control of a multi-agent system where each agent is considered to have a physical shape, unlike a point mass model representation. For simplicity, we consider that the shape of each agent is characterized by a circular disk of the same radii. Leveraging the inter-center distance or interior-angle information among the neighboring agents, that can be easily measured by low-cost vision sensors, we apply constraint on the distance between the two agents such that they avoid collisions as well as maintain connectivity with the neighboring agents. To achieve these constraints on inter-agent distances, we exploit the idea of asymmetric barrier Lyapunov function from the existing literature, and design the stabilizing control law. We show that the agents asymptotically converge to the desired formation and their distances follow the required constraints. Simulations are provided to illustrate the efficacy of the proposed control law.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128289330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Asymptotic Analysis of Discrete-Time Models for Linear Control Systems with Fast Random Sampling 快速随机抽样线性控制系统离散时间模型的渐近分析
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703174
Shivam Dhama, Chetan D. Pahlajani
{"title":"Asymptotic Analysis of Discrete-Time Models for Linear Control Systems with Fast Random Sampling","authors":"Shivam Dhama, Chetan D. Pahlajani","doi":"10.1109/ICC54714.2021.9703174","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703174","url":null,"abstract":"In this paper, we study the dynamics of a linear feedback control system where control is effected via a sample-and-hold implementation of a state-feedback control law, with samples taken at the random event times of a renewal process. Our primary interest is in quantifying, using limit theorems of probability, fluctuations of the system with fast—but finite rate—sampling from its idealized continuously sampled counterpart. Exploiting the linearity and explicit solvability of the system in between samples, questions about the original continuous-time system can be studied through the investigation of an embedded discrete-time stochastic process. The latter records the system state at just the sampling instants, and can be represented in terms of a product of random matrices. We now use limit theorems of the Law of Large Numbers (LLN) and Central Limit Theorem (CLT) type for random matrix products to obtain information about the mean behavior and the typical fluctuations about the mean for the discrete-time process in the limit as the temporal sampling rate goes to infinity.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133857720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Discrete Time Consensus Algorithm In Multi-Agent System 多智能体系统中的离散时间一致性算法
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9702911
Amol Patil, Gautam Shah
{"title":"Discrete Time Consensus Algorithm In Multi-Agent System","authors":"Amol Patil, Gautam Shah","doi":"10.1109/ICC54714.2021.9702911","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9702911","url":null,"abstract":"This paper explains mathematical framework of coordination phenomenon happening in nature and social dynamics. Distributed control law is designed for multi-agent dynamical systems using Perron-Frobenius theory. Algorithm convergence is analyzed for balanced and unbalanced communication graph. The consensus value and the topological condition under which the algorithm converges is also derived and explained. Simulation results shows the effective implementation of proposed algorithm","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Event-Triggered PI Control of Buck Converter in Cyber-Physical Framework 网络物理框架下Buck变换器的事件触发PI控制
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703169
Krishanu Nath, P. Nambisan, Asifa Yesmin, M. K. Bera
{"title":"Event-Triggered PI Control of Buck Converter in Cyber-Physical Framework","authors":"Krishanu Nath, P. Nambisan, Asifa Yesmin, M. K. Bera","doi":"10.1109/ICC54714.2021.9703169","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703169","url":null,"abstract":"This paper presents the voltage control of a DC-DC buck converter connected in a cyber-physical space using an event-triggered PI controller. The communication between the cyber layer and the plant is invoked whenever a threshold condition is violated. The Lyapunov stability of the closed-loop system is established along with a Zeno free behaviour. Finally, the proposed control scheme is applied to a practical system to verify its effectiveness.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133623200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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