A Smart Control Device for Human-Machine Gobang (Tic-Tac-Toe) Game

This work presents the solution based on the augmenting sequence of linear programming problems (LPP) as a tool for intellectualizing home environment. The proposed solution empowers the intelligent decision making procedure which can be applied to various intelligent control applications. The augmenting self-training procedure based on LPP approach is presented as well, which allows making reasonable decisions having only limited data about the controlled environment. The method permits retraining the decision making system when new data is available. As a proof of concept, this solution is applied to intelligent light control application. The obtained simulation results show the method’s capability in making reasonable decisions according to users preferences. DOI: http://dx.doi.org/10.5755/j01.itc.44.2.10235

Bare-bones languages serve small systems with ease: Cushmann, R HEDN Vol 25 No 16 (5 September 1980) pp 171–177

CFAR is a widely used algorithm for target detection in SAR images. The simplicity of computation and stable performance make it a key role in practice. However, for man-made target detection, conventional CFAR has a limited performance because of the non-adaptive processing window and the varieties of categories, sizes and structures of targets. In order to detect man-made targets with different size and complex structures in high resolution SAR images, an improved CFAR algorithm with an adaptive processing window named Adaptive-Window CFAR is proposed. A global guard window obtained by pre-detection adaptively is used to take place of the guard window in conventional CFAR makes AW -CFAR an algorithm with both adaptive threshold and adaptive processing window. In this case, the size of detectable targets is not fixed anymore and targets of different sizes and complex structures are detectable in AW-CF AR. Images with different resolutions and environments, which contain different categories of man-made targets, are used in the experiments. The experimental results show that the AW -CFAR inherits the simplicity of computation and stable performance of the conventional CFAR and has a better performance of man-made target detection in high resolution SAR images.

The machine vision inspection plays a vital role in the digital transformation and intelligent upgrading of the textile industry. Driven by big data and artificial intelligence model, the machine vision inspection is widely·used in the textile industry for quality control, process monitoring, consumer personalized fitting and so on. In order to provide a comprehensive and insightful analysis of recent advances on the machine vision inspection technology in the textile industry to promote the development and bring some insights in this area, this paper presents a systematical review from the essence and applications perspective. Firstly, a technology system of machine vision-based textile inspection is proposed. Then, key technologies of image acquisition, image preprocessing, feature extraction, object detection, and result visualization are discussed. Next, applications of machine vision in the whole textile industry chain including fiber preparation, spinning, weaving, printing and dyeing, and clothing manufacturing are analyzed. Finally, the future challenges and opportunities are highlighted. Through this work, it is hoped to provide a reference for how to choose and apply machine vision technologies in the textile industry, and spark new ideas in the effort to realize intelligent textile inspection.

This research focuses on the benefits of computer vision enhancement through use of an image pre-processing optimization algorithm in which numerous variations of prevalent image modification tools are applied independently and in combination to specific sets of images. The class with the highest returned precision score is then assigned to the feature, often improving upon both the number of features captured and the precision values. Various transformations such as embossing, sharpening, contrast adjustment, etc. can bring to the forefront and reveal feature edge lines previously not capturable by neural networks, allowing potential increases in overall system accuracy beyond typical manual image pre-processing. Similar to how neural networks determine accuracy among numerous feature characteristics, the enhanced neural network will determine the highest classification confidence among unaltered original images and their permutations run through numerous pre-processing and enhancement techniques.

In view of the characteristics of the electrohydraulic servo system, a man-simulated intelligent controller is designed so as to improve the system static precision and dynamic characteristic by application of intelligent control theory in this paper. The results of the emulation and experiment indicate that this controller has good control.

Intelligent control techniques are suited for problems that are either difficult to tackle mathematically or where the use of these techniques provides improved performance, easier implementation, or faster time to market. The authors present some industrial applications of fuzzy logic and neural networks at Rockwell, comprising motor control, autopilot level change control, aircraft autothrottle control, aircraft gain schedule generation, sensor diagnostics, and control of color printing. The authors discuss the driving factors for using fuzzy and neural approaches in these applications and the benefits derived. The authors also comment on how to select the right application of intelligent control techniques to derive maximum commercial payoff.

In recent years, with the rapid development of artificial intelligence, machine vision technology has been widely used in various fields. Traditional cancer detection methods are time-consuming, labor-intensive, and highly dependent on the experience of pathologists; therefore, these methods cannot meet the needs of modern medical treatment. Machine vision overcomes the disadvantages of traditional detection methods in cancer detection and can help pathologists improve the detection accuracy. According to the requirements of medical detection, this review summarizes the applications of machine vision in the detection of cancer cells in histopathological images and analyzes the advantages and disadvantages of existing methods in image preprocessing, segmentation, feature extraction and recognition. Finally, research on the detection methods of histopathological cancer cells is reviewed and prospected, and future development trends are predicted to provide guidance for follow-up research.

The target detection algorithm for accurately locating the location of objects in the image and their categories is widely used in intelligent traffic, forest fire prevention, electric power patrol and other fields. Existing target detection algorithms detect objects in head-up images, and their accuracy is not high when used in aerial image detection. To solve this problem, a small target detection algorithm based on improved RetinaNet model is proposed. First, replace the ResNet101 deep residual network in the original network with ResNet152, Independent of image features, The model is accelerated by merging ScaleNet network structure; Besides, in order to increase the feeling of the small target detection, to upgrade the existing FPN network output, increase the P2 feature layer, improved the range makes the network overall receptive field and the robustness of small target detection. Through the experimental result shows that compared with the original RetinaNet algorithm, The performance index of the improved algorithm is obviously improved. the average detection accuracy of the improved algorithm for small target detection is quite high 6.21%, it has a good detection effect in many scenarios.

Expert System Approach of Industrial Process Adaptive Control

Considering different types of time-varying lag system, control strategy is determined by recognition of transient characteristics of responses and cooperation with expert system, forming the basic idea of intelligent controller design. Based on the dynamic response mode of the controlled object, multi-control mode was applied in control law, which can solve the oscillation problems appeared in common control system during pure lag link and improve the robustness of the control system. The application of intelligent temperature controller based on the pattern recognition in the same category of controlled objects requires no specific parameters and mathematical model of the controlled objects, but can bring a stable increase of manipulated variable, quick response, small overshoot, and no steady-state error, enabling it appropriate for different types of controlled objects and achieving perfect control effect.

The products that we will include in our demonstration are our Flexible Wavelength Selector (FWS) tunable filter devices and Spectrometer (SP series) for output readings with software interface display. The Flexible Wavelength Selector (FWS) is a simple and compact approach to tunable wavelength filtering that combines the wavelength tuning and bandwidth adjustment capabilities of a monochromator with the imaging functionality of a thin-film filter that is ideal for use in both illumination and image acquisition. Due to the modular design, they are intended to be used with a separate broadband light source (LEDs, lasers, or lamps) where input and output connections can be either fiber, fiber-collimated, collimated, or free space. FWS tunable filters have a spectral tuning range from 358-900 nm, varies between models, with an adjustable bandwidth (FWHM) from ~2-16 nm (See spec sheets for more details). FWS devices are ideal for both end user and OEM integration in microscopy and other instrumentation applications across the life sciences – for filtering both sources and detectors. Examples include optogenetics, cell biology, spectroscopic chemical imaging, machine vision, and hyperspectral imaging applications in aerospace, pharma, agriculture, semiconductor, and automotive industries. Compared to existing tunable filtering technologies (AOTFs, Liquid Crystal Tunable Filter, etc.), FWS devices have the following advantages: 1. High Transmission efficiency (>75%) 2. Polarization-independent 3. High OD for out-of-band blocking (>OD6-12) 4. Independent control of center wavelength and bandwidth 5. Large circular aperture for imaging applications For more information visit https://www.spectrolightinc.com/ or contact sales@spectrolightinc.com

In the paper, it is proposed to develop a machine learning based intelligent defect detection system for metal products. The common machine vision system has the surface (stain, shallow pit, shallow tumor, scratches, Edge defects, pattern defects) detection, or for the processing of the size, diameter, diameter, eccentricity, height, thickness and other parts of the non-contact numerical parameters of detection. Considering the quality of the work piece and the defects of the standard, so for the quality of customized testing requirements, the study is the development of machine vision and machine learning metal products defect detection system, mainly composed of three procedures: Image preprocessing, training procedures and testing procedures. The system architecture consists of three parts: (1) Image preprocessing: we first use the machine vision. OPENCV to carry out the image pre-processing part of the product before the detection. (2) Training procedures: The algorithm of the machine learning includes the convolution neural network (CNN), chunk-max pooling is used to train the program, and the generative adversarial network (GAN) based architecture is used to solve the problem of small datasets for surface defects. (3) Testing procedures：The Python language is used to write the program and implement the testing procedures with the GPU-Based embedded hardware In industries, collecting training dataset is usually costly and related methods are highly dataset-dependent. So most companies cannot provide Big-data to be analyzed or applied. By the experimental results, the recognition accuracy can be obviously improved as increasing data augmentation by GAN-Based samples maker. Manual inspection is labor intensive, costly and less in efficiency. Therefore, this study will contribute to technological innovation, industry, national development and other applications. (1) The use of intelligent machine learning technology will make the industry 4.0 technology more sophisticated. (2) It will make the development of equipment industry be better by the machine learning applications. (3) It will increase the economics and productivity of countries for the aging of the population by machine learning.

The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.

Models of manufacturing flexibility have become centred on highly automated systems. These fail to account for the flexibility that is characteristic of many small, human-centred manufacturing operations or in systems based on hybrid automation. This paper presents a generic framework that models flexibility in terms of the underlying capability of a manufacturing system and its control systems, human and machine. In this model, manufacturing versatility, the ability to change, is realized through the application of automatic control to the inherent capability of the system. Flexibility defined as the ability to cope with the uncertainty of change, extends versatility through the application of intelligent control. This process generates new knowledge about the system and its environment. The system learns by applying this knowledge and consequently the autonomic response of the system is dynamically improved. The model is hierarchical, accounting for capability, versatility and flexibility at various levels of manufacturing system, from the operator-machine level up to a whole organization. The efficacy of the model is demonstrated in a study of a semi-manual assembly cell in a small manufacturing company.