There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models: one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

/pdf/hun-he-zeng-qiang-zhi-neng-xie-zuo-yu-ren-zhi-4gpfem6vsq.pdf

混合-增强智能：协作与认知

Magnetoresistive (MR) sensors have been identified as promising candidates for the development of high-performance magnetometers due to their high sensitivity, low cost, low power consumption, and small size. The rapid advance of MR sensor technology has opened up a variety of MR sensor applications. These applications are in different areas that require MR sensors with different properties. Future MR sensor development in each of these areas requires an overview and a strategic guide. An MR sensor roadmap (non-recording applications) was therefore developed and made public by the Technical Committee of the IEEE Magnetics Society with the aim to provide an research and development (R&D) guide for MR sensors intended to be used by industry, government, and academia. The roadmap was developed over a three-year period and coordinated by an international effort of 22 taskforce members from ten countries and 17 organizations, including universities, research institutes, and sensor companies. In this paper, the current status of MR sensors for non-recording applications was identified by analyzing the patent and publication statistics. As a result, timescales for MR sensor development were established and critical milestones for sensor parameters were extracted in order to gain insight into potential MR sensor applications (non-recording). Five application areas were identified, and five MR sensor roadmaps were established. These include biomedical applications, flexible electronics, position sensing and human–computer interactions, non-destructive evaluation and monitoring, and navigation and transportation. Each roadmap was analyzed using a logistic growth model, and new opportunities were predicted based on the extrapolated curve, forecast milestones, and professional judgment of the taskforce members. This paper provides a framework for MR sensor technology (non-recording applications) to be used for public and private R&D planning, in order to provide guidance into likely MR sensor applications, products, and services expected in the next 15 years and beyond.

/pdf/magnetoresistive-sensor-development-roadmap-non-recording-nb32j30mph.pdf

Magnetoresistive Sensor Development Roadmap (Non-Recording Applications)

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

https://hal.archives-ouvertes.fr/hal-00524624/document

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.

/pdf/a-review-of-recent-advances-in-automated-guided-vehicle-qlzg8e9vod.pdf

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

Design of Fuzzy-PID Controller for Path Tracking of Mobile Robot with Differential Drive

This paper describes a neural network model for an electrical differential system for electric vehicle. When a vehicle drives along curved road lane, the speed of the inner wheel has to be different from that of the outer wheel in order to prevent the vehicle vibrating and traveling an unsteady path. Because each wheel of this electrical vehicle has an independent driving force, an electrical differential system is required to replace a gear differential system. However, it is difficult to analysis the nonlinear behavior of the differential system in relation to the vehicle speed and steering angle, as well as vehicle structure. Therefore, a neural network is used to learn the relationships. To realize the neural network model, the speed data was acquired for the inner wheel and outer wheel, using an experimental electric vehicle at various speeds and steering angles. With this information, the differential system can be controlled using a neural network model of the nonlinear relationships.

A neural network model of electric differential system for electric vehicle

An Autonomous Control of Fuzzy-PD Controller for Quadcopter

The torque sensor is used to measure the joint torque of a robot manipulator. Previous research showed that the sensitivity and the stiffness of torque sensors have trade-off characteristics. Stiffness has to be sacrificed to increase the sensitivity of the sensor. In this research, a new torque sensor with high sensitivity (TSHS) is proposed in order to resolve this problem. The key idea of the TSHS comes from its 4-bar linkage shape in which the angular displacement of a short link is larger than that of a long link. The sensitivity of the torque sensor with a 4-bar link shape is improved without decreasing stiffness. Optimization techniques are applied to maximize the sensitivity of the sensor. An actual TSHS is constructed to verify the validity of the proposed mechanism. Experimental results show that the sensitivity of TSHS can be increased 3.5 times without sacrificing stiffness.

https://www.mdpi.com/1424-8220/16/7/991/pdf

Development of Torque Sensor with High Sensitivity for Joint of Robot Manipulator Using 4-Bar Linkage Shape

In this paper, a magnetic position sensor for a magnetic guidance system of automated ground vehicles using linear sections of magnetic fields is proposed. In previous research, the ratio method using vertical versus lateral field was applied to the magnetic position sensor. A drawback of this method is that there is a singularity problem in calculating the ratio as the lateral component approached zero. In this paper, instead of the ratio calculation, the linear method using the equation between vertical magnetic field and the position of a marker is proposed. Due to the linear equation, the proposed method is very robust when noise and disturbance are added to the sensing signal. Since the linear equation is simple, the calculation is fast so that the proposed method is better to establish a real time system. The performance of the proposed method is tested by the experiment using the developed magnetic sensor.

http://ieeexplore.ieee.org/iel5/6375948/6393028/06393369.pdf

Young-Jae Ryoo

Papers

Design of Fuzzy-PID Controller for Path Tracking of Mobile Robot with Differential Drive

A neural network model of electric differential system for electric vehicle

An Autonomous Control of Fuzzy-PD Controller for Quadcopter

Development of Torque Sensor with High Sensitivity for Joint of Robot Manipulator Using 4-Bar Linkage Shape

Design and development of magnetic position sensor for magnetic guidance system of automated ground vehicle