Automatic detonator code recognition via deep neural network

Intelligent computing system based on pattern recognition and data mining algorithms

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

A Modified Flower Pollination Algorithm for Global Optimization

A novel fuzzy curvature method for recognition of anterior forearm subcutaneous veins by thermal imaging

In modern manufacturing industry featured with automation and flexibility, the intelligent tool management for Computer Numeric Control (CNC) machine plays an essential role in manufacturing automation. The automatic tool recognition in terms of geometric shapes, materials and usage functions could facilitate the seamless integration with downstream process planning and scheduling processes. In this paper, a intelligent tool recognition system is proposed with a novel hybrid framework of multi-channel deep learning network with non-iterative and fast feedforward neural network to meet high efficiency and accuracy requirement in intelligent manufacturing. The combination of the fine-tuning Convolutional Neural Networks (CNNs) with the random parameter assignment mechanism of Extreme Learning Machines (ELMs) reach a balance in accurate feature extraction and fast recognition. In the proposed hybrid framework, features extracted from efficient CNNs are aggregated into robust ELM auto-encoders (ELM-AEs) to generate the compact but rich feature information, which are then feed to the subsequent single layer ELM network for tool recognition. The performance of proposed framework is verified on several standardized 3D shape retrieval and classification dataset, as well as on a self-constructed multi-view 3D data represented tool library database. Numerical experiments reveal a promising application perspective of proposed intelligent recognition system on manufacturing automation.

The authors review and categorize the research in applications of artificial intelligence (AI) and expert systems (ES) in new product development (NPD) activities. A brief overview of NPD process and AI is presented. This is followed by a literature survey in regard to AI and ES applications in NPD, which revealed twenty four articles (twenty two applications) in the 1990–1997 period. The applications are categorized into five areas: expert decision support systems for NPD project evaluation, knowledge-based systems (KBS) for product and process design, KBS for QFD, AI support for conceptual design and AI support for group decision making in concurrent engineering. Brief review of each application is provided. The articles are also grouped by NPD stages and seven NPD core elements (competencies and abilities). Further research areas are pointed out.

Learning competitive channel-wise attention in residual network with masked regularization and signal boosting

There are many types of intelligent systems used in modeling the information processing at the human brain level. The information processing is divided into two major parts: first part represents the information processing at the microstructure level (represented by sensory processes) and second part represents the information processing at the macrostructure level (represented by reasoning, memory and problem solving). In this context, the neural networks are used for model the first part of learning process - the primary stage of this and expert systems are used for model the second part of learning process - the final stage of this. So, the information processing is model by a complex system which contains many intelligent control systems: neural network, expert system and hybrid intelligent systems

A methodology is proposed for advanced automation in manufacturing. Building on top of the current CAD/CAM techniques and expert system methods, integrated intelligent systems can realize decision-making automation based on a knowledge processing technique to meet the challenge of the upcoming knowledge-intensive industry. The parallel hierarchical structure of integrated intelligent software systems is developed, and an architecture of the integrated intelligent unit for controlling and managing large-scale intelligent systems is investigated. In an integrated intelligent system, the different expert systems and numerical computation routines are coordinated by a metasystem. These expert systems and numerical routines are written in different languages or programming tools, and used separately. In this way, it is possible to easily add new programs and reduce the scope of rule search to enhance efficiency. >

SERAC (Expert System for Acoustic-phonetic Recognition) is an Expert System that applies Artificial Intelligence techniques to Automatic Speech Recognition.

Discusses artificial intelligence, particularly expert or knowledge systems, and its relevance for business‐to‐business marketing and marketing decision support systems. Enables the reader to understand the basic concepts of expert systems while providing more technical references, before discussing current and potential applications for decision support systems. Argues that expert systems will ultimately play a major role in such applications, developing over the next two decades. Includes appendices and a glossary of terms and definitions.

Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.

Machining sensor data management for operation-level predictive model

Objective To evaluate the application value of an intelligent fundus assisted diagnosis system for detecting retinopathy of prematurity (ROP) based on deep learning. Methods A total of 38 895 fundus images for premature infants screening were collected from Renmin Hospital of Wuhan University Eye Center and were labeled by 10 licensed ophthalmologists.A deep learning network model was established to acquire automatic classification of disease stages and plus disease.The accuracy, sensitivity and specificity of the algorithm were calculated to evaluate the performance of the artificial intelligence system for ROP automatic diagnosis.This study protocol was approved by Ethic Committee of Renmin Hospital of Wuhan University (No.WDRY2019-K032). Written informed consent was obtained from the guardians of the children before entering the study cohort. Results The intelligent system achieved an accuracy of 0.931.Specifically, the accuracies in detecting demarcation line (stage Ⅰ) was 0.876, ridge (stage Ⅱ) was 0.942, ridge with extra retinal fibrovascular (stage Ⅲ) was 0.968, subtotal retinal detachment (stage Ⅳ) was 0.998, total retinal detachment (stage Ⅴ) was 0.999, vascular tortuosity and dilatation (plus disease) was 0.896, optic disc was 0.954, macular was 0.781, and laser scars were 0.974, respectively. Conclusions Deep learning algorithm can detect the stages and plus disease of ROP with excellent accuracy, and it provides the feasibility of applying the algorithm for ROP automated screening in clinical. Key words: Artificial intelligence; Deep learning; Retinopathy of prematurity; Assisted diagnosis

The design of systems for intelligent control of urban traffic is important in providing a safe environment for pedestrians and motorists. Artificial neural networks (ANNs) (learning systems) and expert systems (knowledge-based systems) have been extensively explored as approaches for decision-making. While the ANNs compute decisions by learning from successfully solved examples, the expert systems rely on a knowledge base developed by human reasoning for decision making. It is possible to integrate the learning abilities of an ANN and the knowledge-based decision-making ability of the expert system. This paper presents a real-time intelligent decision-making system, IDUTC, for urban traffic control applications. The system integrates a backpropagation-based ANN that can learn and adapt to the dynamically changing environment and a fuzzy expert system for decision-making. The performance of the proposed intelligent decision-making system is evaluated by mapping the adaptable traffic light control problem. The application is implemented using the ANN approach, the FES approach, and the proposed integrated system approach. The results of extensive simulations using the three approaches indicate that the integrated system provides better performance and leads to a more efficient implementation than the other two approaches.