Automatic Support Research Articles

Energy Optimization for Smart Housing Systems

Smart home is an emerging technology which is growing continuously now. It integrates many of the new technologies with the help of home networking for improving human’s quality and standard of living, so there are many projects which are researching in diverse technologies in order to apply them to the smart home system. As the technology evolves it comforts mankind with some additional ease and advancement. At the time the evolution calls upon all the daily routine devices to operate over internet and this project is based on the idea to make these devices accessible to the owner anytime anywhere. Particularly for now we intend to connect electrical appliances in any house to a kind of a local area network so that it can be operated by the respective authorities so as to minimize the electricity wastage. Further, these devices along with sensors can be made to operate on their own, intelligently and accurately. The excellency of the project can be utilized in hostels and classrooms as well where most of the times we find electrical appliances operational even when not necessary. The idea is to turn a house into a smart house. The project utilizes the current technology such as wifi and low-cost electronic modules to meet the requirements of IOT and gives it a web as well as an app interface for an ease of access. The technologies which we are using include - Wifi mod-ule, Relay module, Raspberry pi, Sensors for automatic support and feedback to user. The project can be extended to cloud also for data storage and providing access to authorized user.

MATHEMATICAL MODEL OF THE ESTIMATION OF THE EFFICIENCY OF THE CONTROL OF RADIO ELECTRONIC COUNTERACTION OF THE AIRPORT STATION OF ACTIVE CONTEMPORARY INTERFERENCES IN THE CONDITIONS OF CREATING A POLARIZATION INTERFACE ON A FIXED PLANE OF IT

In the article, based on the analysis of the use of modern individual on-board electronics and electronic search and guidance systems, it is concluded that one of the promising directions for their further development is the search for methods of not only protection from the means of radio-electronic suppression, but also an active counteraction to these means, the so-called counter-radio electronic counteraction One of the most effective obstacles that can be created by radar detection systems by stations is the active barrier of individual enemy protection - this is a polarizing obstacle, so the issue of reducing the effectiveness of the polarizing effect on the system of automatic support for the direction of the on-board radar weapon control system is quite relevant today. In order to counteract the station's active interference in the conditions of its creation of a polarization obstacle, it is proposed to change the plane of polarization on the receiving side. As a result, a useful signal will be taken in polarization, which does not coincide with the polarization plane of the radiation signal. Failure to match polarization when receiving a reflected signal will not significantly affect the power of a useful signal. In order to implement the proposed method in the article, a mathematical model for evaluating the effectiveness of the counter-radio-electronic counteraction of the on-board station was proposed to obstruct the enemy's individual protection in the conditions of its creation of a polarization obstacle on a fixed plane of polarization, as well as features of the practical realization of such counteraction during air combat and restrictions on its creation. The mathematical model of the evaluation of the effectiveness of the counter-electronic counteraction to the aircraft station's active barriers in the conditions of its creation of a polarization obstacle on a fixed plane of polarization is developed, it will allow to formulate practical recommendations regarding the application of the developed method at different stages of air combat. KELP management in different ways, on the one hand, slightly reduces the effectiveness of the on-board management of weapons at different stages of flight, but on the other hand, the effectiveness of the airborne equipment of the airborne ejection of the enemy significantly decreases, which will increase the effectiveness of the use of weapons and the likelihood of damage to the enemy aircraft using the means of destruction, which today have a significant cost.

Implementation of Classification and Recognition Algorithm for Text Information Based on Support Vector Machine

At present, Internet data is the world’s largest data resource database. In order to realize fast and automatic intelligent classification, it is of great significance to develop automatic classification of public security intelligent data systems. This paper studies the actual needs of public security information text classification, analyzes the text automatic classification technology support vector machine (SVM) theory and designs and implements SVM-based public security information, and also realizes the classification system of public security information. Automatic classification provides support for subsequent text mining systems and text searches and designs the performance of the system. After optimization and testing, the system was found to have good practical application results.

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed.

P839Lifetime cost-effectiveness of diagnostic artificial intelligence tool for evaluating individuals with stable chest pain. The co-operative ARTICA registry database

Abstract Background Non-invasive cardiac imaging testing has been often favored as an initial test for symptomatic patients with at least intermediate pre-test likelihood (pt-lk) of obstructive CAD. Despite this condition, uncertainty remains regarding the optimal testing strategies. It is known that intelligence applied with automatic decision support system (AI DSS) is able to correctly identify absence of significant CAD versus standard care (SD) in patients with stable chest pain (SCP). No evidence of long-term cost-effectiveness about AI DSS has been published in this setting. Purpose The aim is to determine the cost-effectiveness of AI DSS when applied to individuals without known CAD presenting with stable chest pain syndrome. Methods 1725 subjects, 982 males, age 61±12 years, with SCP were referred for clinical evaluation by human standard care (SD) and AI DSS administration during same day visit on a 2 years period. Exercise treadmill test (ETT), coronary tomographic angiography (CTA), invasive coronary angiography (ICA), stress echocardiography (SE)/gated myocardial perfusion scintigraphy (gMPS) and follow up/no tests (FNT) alone and combined strategies were analyzed. For the post-diagnosis follow up period of 16±3 months, we employed a Markov model based on 1-year cycle to account for outcomes for those correctly diagnosed with CAD. All subjects performed CTA to verify presence of CAD. CAD was defined as ≥70% stenosis in at least one major epicardial coronary artery vessels. Monte Carlo simulation was performed to derive mean values for costs and QALYs at different CAD prevalence of 15%, 50% and 80%. Results Data from ARTICA registry about lifelong costs based upon different diagnostic strategies in subjects with 15%, 50% and 80% CAD pt-lk are shown in Table. Lifelong costs related to strategies FNT (€) ETT-SE/gMPS-ICA (€) SE/gMPS-ICA (€) CTA-ICA (€) CTA-SE/gMPS-ICA pt-lk CAD 15% AI DSS 350 8,250 8,850 10,450 11,020 SD 1,015 11,100 12,715 12.215 12,215 pt-lk CAD 50% AI DSS 1,610 17,375 19,540 20,410 20,110 SD 1,855 19,650 21,340 22,950 22,115 pt-lk CAD 80% AI DSS 2,910 28,210 30,875 31,215 31,765 CD 4,110 32,715 34,815 35,755 35,660 Conclusion Data from ARTICA registry demonstrate that automatic use of AI DSS result in improved costs and enhanced effectiveness when compared with human SD in subjects with stable chest pain.

An empirical study on bug propagation through code cloning

Code clones are identical or nearly similar code fragments in a code-base. According to the existing studies, code clones are directly related to bugs. Code cloning, creating code clones, is suspected to propagate temporarily hidden bugs from one code fragment to another. However, there is no study on the intensity of bug-propagation through code cloning. In this paper, we define two clone evolutionary patterns that reasonably indicate bug propagation through code cloning. By analyzing software evolution history, we identify those code clones that evolved following the bug propagation patterns. According to our study on thousands of commits of seven subject systems, overall 18.42% of the clone fragments that experience bug-fixes contain propagated bugs. Type-3 clones are primarily involved with bug-propagation. Bug propagation is more likely to occur in the clone fragments that are created in the same commit rather than in different commits. Moreover, code clones residing in the same file have a higher possibility of containing propagated bugs compared to those residing in different files. Severe bugs can sometimes get propagated through code cloning. Automatic support for immediately identifying occurrences of bug-propagation can be beneficial for software maintenance. Our findings are important for prioritizing code clones for management.

Medical image segmentation using fast discrete curvelet transform and classification methods for MRI brain images

Image segmentation and classification are indispensable steps in the therapeutic image processing. Clustering based methods plays an inevitable role in the segmentation stage of the biomedical images especially for distinguishingcerebrum tumors from MRI brain images which is a very challenging task. This paper introduces a curvelet transform to extract features for the computerized detection of abnormalities in the MR brain images. Statistical and texture feature of the brain image using MRI are extracted in the curvelet domain. As manual segmentation is time consuming, an automatic support system for segmentation and classification of tumor stages is preferred. In this paper, the variants of Fuzzy CMeans (FCM) clustering segmentation such as Adaptive FCM, AMS modified FCM and other methods like genetic algorithm and snake algorithms are dealt for which the features extracted using the Curvelet transform serves as the input. The segmented image serves as the input for classifiers like Support Vector machine, Probabilistic neural network, and ANFIS which predict the inputs into the two possible classes being normal and abnormal.

CAD-based design and pre-processing tools for additive manufacturing

This paper discusses a set of geometry based computational pre-processing algorithms developed for Powder Bed Fusion Additive Manufacturing (PBFAM) processes. To start with, based on an initial part design, an automatic support structure generation module generates customized CAD-based support structures for a given part build orientation. Various additive manufacturing (AM) parameters and Design for Additive Manufacturing (DFAM) metrics are calculated on the fly for assigning producibility scores at different part build orientations. A set of stand-alone computational geometry-based algorithms with associated graphical user interfaces (GUI) are developed for calculating support structure parameters, as well as for detecting and highlighting DFAM features that are difficult to manufacture. These stand-alone tools provide a quantified output for each of the parameters or features, which are then used downstream during the producibility index (PI) calculation. An algorithm that evaluates ease of removing supports during the post-processing phase, and suggests the optimum number of setups needed to remove support structures is developed. Finally, Producibility Index, which is a weighted optimization metric, brings together the quantified outputs of the DFAM analysis, support structure parameters, accessibility analysis and suggests the best build orientations for the given part geometry. All the algorithms are implemented within the Siemens NX modelling environment utilizing C++ and NX API functions. The developed algorithm and tools have been succesfully demonstrated on two sample parts.

Automatic Support Removal for Additive Manufacturing Post Processing

An additive manufacturing (AM) process often produces a near-net shape that closely conforms to the intended design to be manufactured. It sometimes contains additional support structure (also called scaffolding), which has to be removed in post-processing. We describe an approach to automatically generate process plans for support removal using a multi-axis machining instrument. The goal is to fracture the contact regions between each support component and the part, and to do it in the most cost-effective order while avoiding collisions with evolving near-net shape, including the remaining support components. A recursive algorithm identifies a maximal collection of support components whose connection regions to the part are accessible as well as the orientations at which they can be removed at a given round. For every such region, the accessible orientations appear as a ‘fiber’ in the collision-free space of the evolving near-net shape and the tool assembly. To order the removal of accessible supports, the algorithm constructs a search graph whose edges are weighted by the Riemannian distance between the fibers. The least expensive process plan is obtained by solving a traveling salesman problem (TSP) over the search graph. The sequence of configurations obtained by solving TSP is used as the input to a motion planner that finds collision free paths to visit all accessible features. The resulting part without the support structure can then be finished using traditional machining to produce the intended design. The effectiveness of the method is demonstrated through benchmark examples in 3D.

A large-scale empirical exploration on refactoring activities in open source software projects

• Developers do not refactor source code frequently. • Developers adopt refactoring mainly to improve the understandability of source code. • Developers tend to not perform refactor in the proximity of a new system's release. • Refactoring tasks are mainly scheduled after the system's structure is stable. • Source code files are most likely to be refactored by their owners rather than others. Refactoring is a well-established practice that aims at improving the internal structure of a software system without changing its external behavior. Existing literature provides evidence of how and why developers perform refactoring in practice. In this paper, we continue on this line of research by performing a large-scale empirical analysis of refactoring practices in 200 open source systems. Specifically, we analyze the change history of these systems at commit level to investigate: (i) whether developers perform refactoring operations and, if so, which are more diffused and (ii) when refactoring operations are applied, and (iii) which are the main developer-oriented factors leading to refactoring. Based on our results, future research can focus on enabling automatic support for less frequent refactorings and on recommending refactorings based on the developer's workload, project's maturity and developer's commitment to the project.

Work design for airport security officers: Effects of rest break schedules and adaptable automation

This study investigated whether there is empirical support for the current EU regulation mandating breaks of at least 10 min after each period of 20 min continuously reviewing X-ray images in airport security screening. As a second goal, it examined whether providing more autonomy to airport security officers (in the form of spontaneous rest breaks and adaptable automation) would improve their performance and subjective state. Seventy-two student participants had to indicate the presence (or absence) of a threat item (either a gun or a knife) in a series of grey-scaled X-ray images of cabin baggage. Three work-rest schedules were examined: spontaneous breaks (i.e. participants could take breaks at any time), two 5-min breaks and two 10-min breaks during a 1-h testing session. Furthermore, half of the participants were assisted in their task by an adaptable support system offering three levels of automation: (1) no support, (2) cues indicating the presence of a potential threat item, and (3) cues indicating the exact location of a potential threat item. Results showed no performance differences between break regimes, which suggests that there may be viable alternatives to the current EU regulations. It also emerged that providing participants with adaptable automation did not lead to better detection performance but resulted in a less positive response bias than participants without automatic support. Implications for current aviation security regulations are discussed.

К вопросу об обосновании применения комплексирования изображений для систем автоматического сопровождения в условиях сложного фона.

К вопросу об обосновании применения комплексирования изображений для систем автоматического сопровождения в условиях сложного фона.

Machine Learning Support for EU Funding Project Categorization

Abstract European Union reallocates its money to their member states using different kinds of funding. EU member states categorize EU funding projects using their own categorization system. While EU prepared an integrated European categorization system, many EU members do not use it in their reports. This hinders a straightforward fiscal analysis. The article aims at an automatic support for categorization of EU funding projects by Machine Learning. The experiments showed that Support Vector Machines (SVM) is the top performance Machine Learning algorithm for this task. We experimented with the SVM classifier and the results disclosed that by employing this approach we can classify EU funding projects using a lexical description better than a baseline (i.e. the classification to a major class). Further, we experienced that the approach using the natural language translator outperforms the approach using the word sense disambiguation. Finally, we investigated the influence of the length of project description on the performance of the classifier. The results showed that while there was a positive correlation between the length of project description and the classifier performance for project descriptions in English, in the case of project description in Non-English languages the classifier performed better for shorter project descriptions. In future, we plan to build a new online application which would use the classifier on the back-end and a user would get a category recommendation on the front-end using a visualization of the EU categorization system.

Model checking software product lines based on feature slicing

Feature model is a popular formalism for describing the commonality and variability of a software product line in terms of features. Feature models symbolise a presentation of the possible application configuration space, and can be customised based on specific domain requirements and stakeholder goals. As feature models are becoming increasingly complex, it is desired to provide automatic support for customised analysis and verification based on the specific goals and requirements of stakeholders. This paper first presents feature model slicing based on the requirements of the users. We then introduce three-valued abstraction of behaviour models based on the slicing unit. Finally, based on multi-valued model checker, a case study was conducted to illustrate the effectiveness of our approach.

Повышение работоспособности цепных приводов скважинных штанговых насосов применением устройств автоматического поддержания натяжения цепи преобразующего механизма

Повышение работоспособности цепных приводов скважинных штанговых насосов применением устройств автоматического поддержания натяжения цепи преобразующего механизма

Nodal Farmers’ Motivations for Exchanging Sorghum Seeds in Northwestern Ethiopia

One of the main challenges of Ethiopian agriculture is the shortage of certified seeds of improved varieties, which results in uneven dispersal of quality seed amongst farmers. In a context where 80% to 90% of the seed requirement is covered by the informal seed sector, understanding how and why seeds are exchanged through informal channels is crucial. This study aims to describe why nodal farmers disseminate seeds at a higher rate than other farmers in their network. Following a social network analysis, in-depth surveys were conducted with identified nodal and connector sorghum farmers in order to determine the main social characteristics that differentiate them from other farmers in a western lowlands community of the Tigray Region of Ethiopia. We examined empirically the main factors that motivate them, the main drawbacks they experience, and the behavioral decisions that could potentially speed up or slow down the adoption of newly released improved varieties of sorghum. The study showed that, in this district, few significant socio-demographic differences exist between nodal and non-nodal farmers. The seed exchange network was hyper localized, as the majority of exchanges took place within village boundaries. Focus group discussions showed that a nodal position should not be taken for granted, as the network is dynamic and in constant evolution. In-depth interviews revealed that it was unlikely for accessing farmers to be consistently denied seeds due to a deeply rooted social norm insisting that one should not, under any circumstances, be turned down when asking for seeds. However, in practice, chronic seed insecure farmers suffering from poor performances may find themselves unable to access quality seeds, as automatic support should not be assumed. In terms of motivation, nodal farmers ranked maintaining friendships and relationships as the two most important. Thus, beyond the risk-sharing mechanism underlying much of the seed exchange, it is a mix of personal and community interests that motivates nodal farmers to have more exchange partners and thus disseminate more seeds on average than other farmers in the seed networks. This indicates that their social capital is the major driver to exchange seeds.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability

The present study evaluated three automation modes for improving performance in an X-ray luggage screening task. One hundred and forty participants were asked to detect the presence of prohibited items in X-ray images of cabin luggage. Twenty participants conducted this task without automatic support (control group), whereas the others worked with either indirect cues (system indicated the target presence without specifying its location), or direct cues (system pointed out the exact target location) or adaptable automation (participants could freely choose between no cue, direct and indirect cues). Furthermore, automatic support reliability was manipulated (low versus high). The results showed a clear advantage for direct cues regarding detection performance and response time. No benefits were observed for adaptable automation. Finally, high automation reliability led to better performance and higher operator trust. The findings overall confirmed that automatic support systems for luggage screening should be designed such that they provide direct, highly reliable cues.Practitioner summary: The present study confirmed previous findings showing better detection performance in X-ray images of luggage when supported by automation providing direct, highly reliable cues. Furthermore, participants used adaptable automation only to select their preferred level of automation. This behaviour did not provide the benefits expected under adaptable automation.

MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis

The survival rate of Liver tumor patients can be improved if we perform early detection and treating them. The clinical researches have exposed that the volume measurement can give the best reflection of the tumor response. The liver tumor requires the tumor segmentation. This paper proposes an automatic support system for stage classification using artificial neural network (learning machine) and to detect Liver Tumor through fuzzy clustering methods for medical application. The detection of the Liver Tumor is a challenging problem, due to the structure of the Tumor cells. This project presents a segmentation method, fuzzy clustering algorithm, for segmenting Magnetic Resonance images to detect the Liver Tumor in its early stages and to analyze anatomical structures.