Advanced Intelligent Systems

Journal Information
ISSN / EISSN : 2640-4567 / 2640-4567
Current Publisher: Wiley (10.1002)
Former Publisher:
Total articles ≅ 442
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Latest articles in this journal

Fu-Xiang Liang, I-Ting Wang,
Published: 3 June 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202100007

Abstract:
The sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy-efficient and compact computing hardware that mimics the biological neural networks. Recently, the neural firing properties have been widely explored in various spiking neuron devices, which could emerge as the fundamental building blocks of future neuromorphic/in-memory computing hardware. By leveraging the intrinsic device characteristics, the device-based spiking neuron has the potential advantage of a compact circuit area for implementing neural networks with high density and high parallelism. However, a comprehensive benchmark that considers not only the device but also the peripheral circuit necessary for realizing complete neural functions is still lacking. Herein, the recent progress of emerging spiking neuron devices and circuits is reviewed. By implementing peripheral analog circuits for supporting various spiking neuron devices in the in-memory computing architecture, the advantages and challenges in area and energy efficiency are discussed by benchmarking various technologies. A small or even no membrane capacitor, a self-reset property, and a high spiking frequency are highly desirable.
Xin Shu, Sameera Sansare, Di Jin, Xiangxiang Zeng, Kai-Yu Tong, , Renjie Zhou
Published: 2 June 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202000277

Abstract:
Leukocyte differential test is a widely carried out clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Herein, an artificial-intelligence-enabled reagent-free imaging hematology analyzer (AIRFIHA) modality is reported that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of isolated leukocytes acquired from a custom-built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, not only high accuracy is achieved in differentiating B and T lymphocytes, but also CD4 and CD8 T cells are classified, therefore outperforming the classification accuracy of most current hematology analyzers. The performance of AIRFIHA in a randomly selected test set is validated and is cross-validated across all blood donors. Due to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.
Published: 2 June 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202100025

Abstract:
Reliable image perception is critical for living organisms. Biologic sensory organs and nervous systems evolved interdependently to allow apprehension of visual information regardless of spatial orientation. By contrast, convolutional neural networks usually have limited tolerance to rotational transformations. There are software-based approaches used to address this issue, such as artificial rotation of training data or preliminary image processing. However, these workarounds require a large computational effort and are mostly done offline. This work presents a bioinspired, robotic vision system with inherent rotation-invariant properties that may be taught either offline or in real time by feeding back error indications. It is successfully trained to counter the move of a human player in a game of Paper Scissors Stone. The architecture and operation principles are first discussed alongside the experimental setup. This is followed by performance analysis of pattern recognition under misaligned and rotated conditions. Finally, the process of online, supervised learning is demonstrated and analyzed.
, Emmanuel Dean-Leon, Julio Rogelio Guadarrama-Olvera, Florian Bergner,
Published: 30 May 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202100038

Abstract:
For physical human–robot interaction (pHRI) where multi-contacts play a key role, both robustness to achieve robot-intended motion and adaptability to follow human-intended motion are fundamental. However, there are tradeoffs during pHRI when their intentions do not match. This paper focuses on bipedal walking control during pHRI, which handles such tradeoff when a human and a humanoid robot having different footsteps locations and durations. To resolve this, a force-reactive walking controller is proposed by adequately combining ankle and stepping strategies. The ankle strategy maintains the robot's intention based on an analytically-optimal center of pressure, leading the robot to oppose resistance to multiple contacts from the human. Based on the robot's kinodynamic constraints and/or the confidence of the robot's intention, the stepping strategy updates the robot's footsteps based on the human's intention implied by the multiple contact forces. Consequently, the proposed walking control on pHRI mutually exchanges human–robot intentions in real-time, thereby achieving coordinated steps. With a full-sized humanoid robot that is able to detect multi-contacts in real-time, we succeeded in performing a long-term “box-step” with multi-contacts pHRI, demonstrating the robustness of our approach.
Yuyang Ji, Congcong Luan, Xinhua Yao, Jianzhong Fu,
Published: 30 May 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202000271

Abstract:
Recently, considerable achievements have been made with the advancements of smart structures, which are known for their controlled deformation, self-repair, and sensing characteristics. Such capabilities have significant potential in the field of bionics. 3D printing methods have revolutionized the high-resolution integrated manufacturing of complex smart structures, resulting in new types of soft robots, actuators, wearable flexible electronics, and biomedical equipment. There is therefore a need for academia and industry to receive an update on the status of these tools. For this reason, herein, a comprehensive overview of the latest progress in printing methods, materials, and applications of various smart structures is provided. Temperature- and electromagnetic-responsive smart structures are highlighted, in addition to self-healing and smart-sensing devices. Current exigencies and future development trends of 3D printing methods and smart structures are also summarized.
Rudra Mukherjee, Priyanka Ganguly,
Published: 26 May 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202100036

Abstract:
On-board sources of energy are critically needed for autonomous robots to work in unstructured environments for extended periods. Thus far, the power requirement of robots has been met through lead-acid and Li-ion batteries and energy harvesters. However, few advances such as light weight, the shape, and size of the batteries used in robotics have remained unchanged for several decades, even though if the research in energy storage has led to devices with flexible form factors. Besides being slow at adopting new energy technologies, robotics also appears to have settled with the idea of centralized energy, as evident from the battery backpack designs of several humanoids. This is in contrast with the biological world, where energy sources are distributed all over the body. Although several attempts have been made to imitate the distributed tactile skin, the energy distribution has strangely not caught attention. A robotic platform can benefit from increased energy density, lesser design complexities, improved body dynamics, and operational reliability with distributed energy. By focusing on the distributed energy, herein, the first comprehensive review supporting the benefits of bioinspired distributed energy in robotics and various energy-storage and energy-harvesting technologies that are available or are tuned to attain the same are presented.
William P. Weston-Dawkes, Iman Adibnazari, Yi-Wen Hu, Michael Everman, ,
Published: 25 May 2021
by Wiley
Advanced Intelligent Systems; doi:10.1002/aisy.202100001

Abstract:
Controllable adhesion has the capability to enable mobile robots to move freely across vertical and inverted surfaces for applications such as inspection, exploration, and cleaning. Previous methods for generating controllable adhesion have relied on fluidic adhesion through suction forces, electromagnetic adhesion through magnetic or electrical interactions, or dry fibrillar structures. Herein, a new method for achieving controllable adhesion by vibrating a flexible plate near a surface, which generates a strong and controllable attraction force, is presented. This adhesion mechanism has the unique property of providing strong adhesion normal to a surface, but very low resistance to motion parallel to the surface, making it attractive for mobile robots. Adhesive capabilities of vibration-based adhesion (VBA) to characterize adhesive force dependence on vibration frequency and surface size are studied. Spatial pressure measurements within the adhesive zone, in combination with visualization of surface vibration modes, demonstrate that adhesion is localized to the center of the disk and decreases radially. A mobile robot to highlight the capabilities and robustness of VBA for payload transport, climbing to inversion transitions, and adhesion control is developed. Overall, a novel physical mechanism for robot-surface adhesion that is robust, controllable, and enables rapid low-friction locomotion is presented herein.
Hyunwoo Kim, JiHyeong Ma, Minki Kim, Jongseok Nam,
Published: 24 May 2021
by Wiley
Advanced Intelligent Systems, Volume 3; doi:10.1002/aisy.202170045

Jiaojiao Wang, Xiaotian Zhang, Junehu Park, Insu Park, Evin Kilicarslan, Yongdeok Kim, Zhi Dou, ,
Published: 24 May 2021
by Wiley
Advanced Intelligent Systems, Volume 3; doi:10.1002/aisy.202170049

Xin Ma, , Song Liu, Jingwen Zhang, Shengnan Liu, Teng Cao, Wenbin Lin, Dong Wu, Natan Roberto de Barros, Mohammad Reza Zare, et al.
Published: 24 May 2021
by Wiley
Advanced Intelligent Systems, Volume 3; doi:10.1002/aisy.202170047

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