A high-level design process for neural-network controls through a framework of human personalities

2021 ◽  
pp. 1-34
Author(s):  
M. Khalghollah ◽  
C.J.B. Macnab
Keyword(s):  
Neural Network ◽  
Design Process ◽  
Level Design ◽  
High Level

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Application of an Artificial Neural Network to Automate the Measurement of Kinematic Characteristics of Punches in Boxing

2021 ◽  
Vol 11 (3) ◽  
pp. 1223
Author(s):  
Ilshat Khasanshin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinematic Parameters ◽  
Constant Control ◽  
Measurement Units ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Effective Development ◽  
High Level ◽  
Different Levels

This work aimed to study the automation of measuring the speed of punches of boxers during shadow boxing using inertial measurement units (IMUs) based on an artificial neural network (ANN). In boxing, for the effective development of an athlete, constant control of the punch speed is required. However, even when using modern means of measuring kinematic parameters, it is necessary to record the circumstances under which the punch was performed: The type of punch (jab, cross, hook, or uppercut) and the type of activity (shadow boxing, single punch, or series of punches). Therefore, to eliminate errors and accelerate the process, that is, automate measurements, the use of an ANN in the form of a multilayer perceptron (MLP) is proposed. During the experiments, IMUs were installed on the boxers’ wrists. The input parameters of the ANN were the absolute acceleration and angular velocity. The experiment was conducted for three groups of boxers with different levels of training. The developed model showed a high level of punch recognition for all groups, and it can be concluded that the use of the ANN significantly accelerates the collection of data on the kinetic characteristics of boxers’ punches and allows this process to be automated.

scholarly journals Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Richard Evan Sutanto ◽  
Sukho Lee
Keyword(s):  
Neural Network ◽  
Attack Detection ◽  
Detection Methods ◽  
Defense System ◽  
Image Prior ◽  
The Neural Network ◽  
Adversarial Examples ◽  
Deep Image ◽  
Adversarial Attack ◽  
High Level

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.

scholarly journals Study on Support System for Materials Selection in the Design Process. 1st Report, Application of Neural Network.

1994 ◽  
Vol 60 (574) ◽  
pp. 2184-2190 ◽  
Author(s):  
Masanori Takuma ◽  
Toshiroh Shibasaka ◽  
Toshio Teshima ◽  
Yoshiro Iwai ◽  
Tomomi Honda
Keyword(s):  
Neural Network ◽  
Design Process ◽  
Support System ◽  
Materials Selection

scholarly journals Methodology of synchronization among strategy and operation. A standards-based modeling approach

2017 ◽  
Vol 37 (2) ◽  
pp. 111-119
Author(s):  
VICTOR EDWIN COLLAZOS ◽  
HELGA DUARTE AMAYA
Keyword(s):  
Information Technology ◽  
Information System ◽  
Enterprise Architecture ◽  
Business Processes ◽  
Modeling Approach ◽  
Operational Level ◽  
Alignment Process ◽  
Level Design ◽  
High Level ◽  
Help Model

Enterprise Architecture (EA) has gained importance in recent years, mainly for its concept of “alignment” between the strategic and operational levels of organizations. Such alignment occurs when Information Technology (IT) is applied correctly and timely, working in synergy and harmony with strategy and the operation to achieve mutually their own goals and satisfy the organizational needs.Both the strategic and operational levels have standards that help model elements necessary to obtain desired results. In this sense, BMM and BPMN were selected because both have the support of OMG and they are fairly well known for modelling the strategic level and operational level, respectively. In addition, i* modeling goal can be used for reducing the gap between these two standards. This proposal may help both the high-level design of the information system and to the appropriate identification of the business processes that will support it.This paper presents a methodology for aligning strategy and the operation based on standards and heuristics. We have made a classification for elements of the models and, for some specific cases, an extension of the heuristics associated between them. This allows us to propose methodology, which uses above-mentioned standards and combines mappings, transformations and actions to be considered in the alignment process.

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