A workpiece detection method based on fusion of deep learning and image processing is proposed. Firstly, the workpiece bounding boxes are located in the workpiece images by YOLOv3, whose parameters are compressed by an improved convolutional neural network residual structure pruning strategy. Then, the workpiece images are cropped based on the bounding boxes with cropping biases. Finally, the contours and suitable gripping points of the workpieces are obtained through image processing. The experimental results show that mean Average Precision (mAP) is 98.60% for YOLOv3, and 99.38% for that one by pruning 50.89% of its parameters, and the inference time is shortened by 31.13%. Image processing effectively corrects the bounding boxes obtained by deep learning, and obtains workpiece contour and gripping point information.
Parallel evolutionary algorithms have been used for solving multiobjective optimization problems. The aim is to find or approximate the Pareto optimal set in a reasonable time. In this work, we present a new approach that divides the objective search-space into different partitions and assigns each processor its corresponding partition. Each processor will try to find the set of solutions for its partition only. The sub-Pareto fronts will be combined later and the parallelisation approach is based on a mutli-start approach by having independent algorithm on every processor with its own starting points. Experimental results on well known test cases showed that the proposed method outperformed several state-of-the-art evolutionary algorithms regarding convergence to the true Pareto front and gave very competitive results when considering the hypervolume metric. Also, superlinear speedup results were achieved for all test functions.
The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.
With the ever-increasing global population and average life expectancy, care homes and care at home services are continuously being stretched beyond capacity. Recent developments in tactile sensing have enabled robot systems to measure human vital signs such as beats per minute (BPM), Respiratory Rate (RR) and Capillary Refill Time (CRT). Using robotic systems to measure vital sign data in the home of an elderly or disabled person would greatly assist medical and health services. This paper proposes the use of a vital sign measuring robotic system together with Cloud computing to intelligently process big data and ascertain the current health status of the service user without the need to expose their identity or burden health professionals. Furthermore, a method that enables medical professionals to visualise the data for a complete geographical region as well as for individual patients is presented and hence we provide details of a closed loop system to support ageing-in-place.
Determining the receptive field of a retinal ganglion cell is critically important when formulating a computational model that maps the relationship between the stimulus and response. This process is traditionally undertaken using reverse correlation to estimate the receptive field. By stimulating the retina with artificial stimuli, such as alternating checkerboards, bars or gratings and recording the neural response it is possible to estimate the cell's receptive field by analysing the stimuli that produced the response. Artificial stimuli such as white noise is known to not stimulate the full range of the cell's responses. By using natural image stimuli, it is possible to estimate the receptive field and obtain a resulting model that more accurately mimics the cells' responses to natural stimuli. This paper extends on previous work to seek further improvements in estimating a ganglion cell's receptive field by considering that the receptive field can be divided into subunits. It is thought that these subunits may relate to receptive fields which are associated with bipolar retinal cells. The findings of this preliminary study show that by using subunits to define the receptive field we achieve a significant improvement over existing approaches when deriving computational models of the cell's response.
In order to reduce the levelised cost of energy (LCOE) of the offshore wind farm, many optimization works should be done to reduce the investment and increase the energy production. As one of the main expenses, the electrical system can take up more than 15% of the total investment while cable costs take a large proportion. In order to make a cost-effective wind farm, the cable connection layout should be optimized. This paper proposes a novel way for offshore wind farm cable connection layout design. The LCOE, which concerns three aspects: electrical power losses, power captured by wind turbines (WT) and investment, is selected to set up the objective function. Since all the optimization variables are integers, a heuristic algorithm, integer particle swarm optimization algorithm (IPSO), is adopted to find a near optimal solution. To improve the performance of the IPSO, an adaptive method for parameter control is used to help to find a better solution. Comparisons are made with results obtained by the Norwegian center for offshore wind energy (NORCOWE) reference wind farm and the presented method. From the simulation, it can be noticed that the presented approach can help to find a cable connection scheme which can reduce the LCOE by 1.75%.
Due to the change in life style and diet, modern people suffer from obesity, diabetes, and other types of diseases. Regular practice of exercise can alleviate the negative effects from the diseases and even cure the diseases in certain cases. In addition, regular practice of exercise improves the quality of life. These facts have drawn much attention and people nowadays recognize the importance of exercise. As a result, more and more people hope to start exercising but they lack the knowledge of how and what to exercise. Professional counseling costs relatively expensive and thus it is difficult for ordinary people to access a counselor. To tackle these issues we propose a fuzzy expert system that designs a workout program. The system receives user's body information, preference on exercise style, and available time. Then, the system generates a customized workout program based on fuzzy reasoning. We conduct experiments to verify the performance of the proposed system. The participants enter their body condition, preference and available time and receive customized workout programs from the system. The experiments verifies the applicability of the system. The future research includes the extension of the system to meet various user demands and to reflect a number of expert knowledge sources.
Various kinds of evolutionary algorithms have been developed to solve multi-objective optimization problems. One of them is multi-objective quantum-inspired evolutionary algorithm (MQEA) which utilizes quantum computing concepts to search the solution space effectively. MQEA used nondominated sorting and crowding distance calculation as the selection operator. This paper proposes MQEA with another kind of selection operator. The proposed RN-MQEA uses reference point-based nondominated sorting approach as the selection operator, which is adopted from NSGA-III. In the computer simulations, RN-MQEA is found to provide more diverse solutions compared to MQEA and NSGA-III in solving the DTLZ test problems.
Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic memory. Deep ART has an attribute field in the bottom layer, which is newly designed to get semantic information of inputs. After encoding all inputs with their features, events are categorized in the event field using specified inputs. Since an episode is made of a temporal sequence of events, Deep ART makes event sequences with proposed sequence encoding and decoding processes. They can encode any temporal sequence of events, even if there are duplicated events in the episode. Moreover, based on the result of the analysis of retrieval error, Deep ART does not use the complement coding for partial inputs to enhance the accuracy of episode retrieval from partial cues. The simulation results demonstrate the effectiveness of Deep ART as the long term memory.
Multimedia recommendation technology has been developed in various fields these days. In order to provide multimedia in addition to dialog, it is essential to select appropriate multimedia associated with a certain situation for more delivery effects of digital storytelling, which enables story telling agents to share their stories with users using digital multimedia in an effective way. For this purpose, we propose a multimedia recommendation system for software agents of smart devices to select multimedia that is appropriate to the given situation in storytelling to users or interacting with users. The fusion ART network is employed for the multimedia recommendation system that selects an appropriate digital media file for individual multimedia features. The system is learned incrementally based on feedback from users. The proposed system is purposed to select multimedia to be conveyed in addition to the dialog between the user and the digital creature on a smartphone. The applicability is verified through experiments with a smartphone application implemented for demonstration.
Episodic memory is the memory of personal experiences as episodes with subjective time. Task memory is defined as a memory for storing the knowledge of sequential procedures to perform tasks. Rather than encoding and retrieving such a temporal sequence of events or procedures, respectively, it is more efficient to implement both memories into a single memory model together. For this purpose, this paper proposes an integrated adaptive resonance theory (I-ART) neural model for episodic memory with task memory. The performance of the proposed episodic memory model is confirmed through comparison study with the other methods. And the proposed task memory is applied to perform tasks by Mybot-KSR2, developed in RIT Lab., KAIST.
Research on synthetic aperture radar (SAR) scene matching in the aircraft end-guidance has a significant value for both research and real-world application. The conventional scene matching methods, however, suffer many disadvantages such as heavy computation burden and low convergence rate so that these methods cannot meet the requirement of end-guidance system in terms of fast and real-time data processing. Furthermore, there are complex noises in the SAR image, which also compromise the effectiveness of using the conventional scene matching methods. To address the above issues, in this paper, the intelligent optimization method, Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration, has been introduced to tackle the SAR scene matching problem. We first establish the effective similarity measurement function for target edge feature matching through introducing the edge potential function (EPF) model. Then, a new method, ADEQFS-EPF, has been proposed for SAR scene matching. In ADEQFS-EPF, the previous studied theoretical model, ADEQFS, is combined with EPF model. We also employed three recent proposed evolutionary algorithms to compare against the proposed method on optical and SAR datasets. The experiments based on Matlab simulation have verified the effectiveness of the application of ADEQFS and EPF model to the field of SAR scene matching.
To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position control problem. Specifically, the dynamic growing mechanism is considered in more detail. In contrast to other self-organizing methods, DGFNC adds new rules more conservatively; hence the pruning mechanism is omitted. Instead, the adaptive strategy ‘adapts’ the control system to parameter variation. Furthermore, a sliding mode-based nonlinear controller ensures system stability. The resulting general control strategy aims to achieve faster response with less computation while maintaining overall stability. Finally, the 3PSP is chosen due to its complex dynamics and the utility of such approaches in modern industrial systems. Several simulations support the merits of the proposed DGFNC strategy as applied to the 3PSP robot.
In this paper, reactive vehicle navigation in unknown environment with obstacles, using fuzzy controller, is presented. In order to prevail conventional fuzzy controller drawbacks, analytical fuzzy controller is proposed. Reference trajectory is generated in real-time using potential field method. Vehicle dynamics model is full nonlinear, eleventh order model, which includes three degrees of freedom, wheel dynamics and nonlinear tyre friction model. Controller capabilities are tested depending on different vehicle's initial velocity and different road conditions.
Existing solutions for dead reckoning cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. We designed two different types of extended Kalman filter (EKF) to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy compared to the conventional multiple model approach.
