A Survey of Nature-Inspired Computing Song, Bosheng; Li, Kenli; Orellana-Martín, David ...
ACM computing surveys,
04/2021, Volume:
54, Issue:
1
Journal Article
Peer reviewed
Nature-inspired computing
is a type of human-designed computing motivated by nature, which is based on the employ of paradigms, mechanisms, and principles underlying natural systems. In this article, ...a versatile and vigorous bio-inspired branch of natural computing, named
membrane computing
is discussed. This computing paradigm is aroused by the internal membrane function and the structure of biological cells. We first introduce some basic concepts and formalisms of membrane computing, and then some basic types or variants of
P systems
(also named
membrane systems
) are presented. The state-of-the-art computability theory and a pioneering computational complexity theory are presented with P system frameworks and numerous solutions to hard computational problems (especially
NP
-complete problems) via P systems with membrane division are reported. Finally, a number of applications and open problems of P systems are briefly described.
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Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart ...Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.
The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications ...focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.
•A strategic vision of the new paradigm of cloud-based manufacturing systems.•A review of the current implementation state in industry.•A road map for future academic research required to advance the ...field.
Cloud manufacturing, a service oriented, customer centric, demand driven manufacturing model is explored in both its possible future and current states. A unique strategic vision for the field is documented, and the current state of technology is presented from both industry and academic viewpoints. Key commercial implementations are presented, along with the state of research in fields critical to enablement of cloud manufacturing, including but not limited to automation, industrial control systems, service composition, flexibility, business models, and proposed implementation models and architectures. Comparison of the strategic vision and current state leads to suggestions for future work, including research in the areas of high speed, long distance industrial control systems, flexibility enablement, business models, cloud computing applications in manufacturing, and prominent implementation architectures.
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This paper tackles the cross-modality person re-identification (re-ID) problem by suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images are in different ...modalities. Given a training identity, the popular deep classification baseline shares the same proxy (i.e., a weight vector in the last classification layer) for two modalities. We find that it has considerable tolerance for the modality gap, because the shared proxy acts as an intermediate relay between two modalities. In response, we propose a Memory-Augmented Unidirectional Metric (MAUM) learning method consisting of two novel designs, i.e., unidirectional metrics, and memory-based augmentation. Specifically, MAUM first learns modality-specific proxies (MS-Proxies) independently under each modality. Afterward, MAUM uses the already-learned MS-Proxies as the static references for pulling close the features in the counterpart modality. These two unidirectional metrics (IR image to RGB proxy and RGB image to IR proxy) jointly alleviate the relay effect and benefit cross-modality association. The cross-modality association is further enhanced by storing the MS-Proxies into memory banks to increase the reference diversity. Importantly, we show that MAUM improves cross-modality re-ID under the modality-balanced setting and gains extra robustness against the modality-imbalance problem. Extensive experiments on SYSU-MMOI and RegDB datasets demonstrate the superiority of MAUM over the state-of-the-art. The code will be available.
Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better ...safety for drivers and passengers. This paper proposes a distributed motion planning algorithm to ensure safe overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field method. Unlike the conventional overtaking techniques, autonomous driving strategies can be used to implement safe overtaking via formation control of unmanned vehicles in a complex vehicle platoon in the presence of human-operated vehicles. Firstly, we formulate the overtaking problem of a group of autonomous vehicles into a multi-target tracking problem, where the targets are dynamic. To model a multi-vehicle system consisting of both autonomous and human-operated vehicles, we introduce the notion of velocity difference potential field and acceleration difference potential field. We then analyze the stability of the multi-lane vehicle platoon and propose an optimization-based algorithm for solving the overtaking problem by placing a dynamic target in the traditional artificial potential field. A simulation case study has been performed to verify the feasibility and effectiveness of the proposed distributed motion control strategy for safe overtaking in a multi-lane vehicle platoon.
This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a ...rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.
•Adaptation of the IEEE34 BUS model to the Brazilian electricity sector standard.•Validation of adapted IEEE34 model with insertion of distributed energy resources.•Robust and simplified model for ...power quality analysis on the electric power system.•Development of a simulation methodology with integration between ATPdraw and MATLAB.•Methodology to optimize the analysis and impacts of distributed energy resources.
Due to the increasing use of electric power distribution resources based on inverters, emerged a concern about the power quality in distribution systems. This is because these new elements can insert voltage or current imbalance in the system due to the configuration change and the presence of power electronics. The main disturbances caused by this equipment are voltage imbalance, harmonic distortion and long-term voltage variation. Therefore, this research aimed to analyze the power quality of distributed systems considering the variation in penetration and allocation of distribution generation and batteries represented by the output of the inverters. The IEEE 34 bus system was used for analyzing, adapted to the Brazilian electrical system, and considering low voltage branches. The results obtained showed that for each electromagnetic disturbance there is an allocation that results in smaller insertions of this disturbance, and it is not possible to have an allocation, among those analyzed, that help to mitigate all disturbances in the system. The penetration of distributed generation directly affects the power quality disturbances, causing some harmonics to increase. As for the long-term voltage variation and voltage imbalance, it depends on the system configuration.
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Blockchain technology has been gaining visibility owing to its ability to enhance the security, reliability, and robustness of distributed systems. Several areas have benefited from research based on ...this technology, such as finance, remote sensing, data analysis, and healthcare. Data immutability, privacy, transparency, decentralization, and distributed ledgers are the main features that make blockchain an attractive technology. However, healthcare records that contain confidential patient data make this system very complicated because there is a risk of a privacy breach. This study aims to address research into the applications of the blockchain healthcare area. It sets out by discussing the management of medical information, as well as the sharing of medical records, image sharing, and log management. We also discuss papers that intersect with other areas, such as the Internet of Things, the management of information, tracking of drugs along their supply chain, and aspects of security and privacy. As we are aware that there are other surveys of blockchain in healthcare, we analyze and compare both the positive and negative aspects of their papers. Finally, we seek to examine the concepts of blockchain in the medical area, by assessing their benefits and drawbacks and thus giving guidance to other researchers in the area. Additionally, we summarize the methods used in healthcare per application area and show their pros and cons.
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Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that ...information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms.
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