ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Acupuncture with manual and electrical stimulation for labour pain : a longitudinal randomised controlled trial

Background: Acupuncture is commonly used to reduce pain during labour despite contradictory results. The aim of this study is to evaluate the effectiveness of acupuncture with manual stimulation and acupuncture with combined manual and electrical stimulation (electro-acupuncture) compared with standard care in reducing labour pain. Our hypothesis was that both acupuncture stimulation techniques were more effective than standard care, and that electro-acupuncture was most effective.  Methods: A longitudinal randomised controlled trial. The recruitment of participants took place at the admission to the labour ward between November 2008 and October 2011 at two Swedish hospitals. 303 nulliparous women with normal pregnancies were randomised to: 40 minutes of manual acupuncture (MA), electro-acupuncture (EA), or standard care without acupuncture (SC). Primary outcome: labour pain, assessed by Visual Analogue Scale (VAS). Secondary outcomes: relaxation, use of obstetric pain relief during labour and post-partum assessments of labour pain. The sample size calculation was based on the primary outcome and a difference of 15 mm on VAS was regarded as clinically relevant, this gave 101 in each group, including a total of 303 women.  Results: Mean estimated pain scores on VAS (SC: 69.0, MA: 66.4 and EA: 68.5), adjusted for: treatment, age, education, and time from baseline, with no interactions did not differ between the groups (SC vs MA: mean difference 2.6, 95% confidence interval [CI] -1.7-6.9 and SC vs EA: mean difference 0.6 [95% CI] -3.6-4.8). Fewer number of women in the EA group used epidural analgesia (46%) than women in the MA group (61%) and SC group (70%) (EA vs SC: odds ratio [OR] 0.35; [95% CI] 0.19-0.67).  Conclusions: Acupuncture does not reduce women's experience of labour pain, neither with manual stimulation nor with combined manual and electrical stimulation. However, fewer women in the EA group used epidural analgesia thus indicating that the effect of acupuncture with electrical stimulation may be underestimated. These findings were obtained in a context with free access to other forms of pain relief.

Acupuncture with manual and electrical stimulation for labour pain : a two month follow up of recollection of pain and birth experience.

BACKGROUND: In a previous randomised controlled trial we showed that acupuncture with a combination of manual- and electrical stimulation (EA) did not affect the level of pain, as compared with acupuncture with manual stimulation (MA) and standard care (SC), but reduced the need for other forms of pain relief, including epidural analgesia. To dismiss an under-treatment of pain in the trial, we did a long-term follow up on the recollection of labour pain and the birth experience comparing acupuncture with manual stimulation, acupuncture with combined electrical and manual stimulation with standard care. Our hypothesis was that despite the lower frequency of use of other pain relief, women who had received EA would make similar retrospective assessments of labour pain and the birth experience 2 months after birth as women who received standard care (SC) or acupuncture with manual stimulation (MA). METHODS: Secondary analyses of data collected for a randomised controlled trial conducted at two delivery wards in Sweden. A total of 303 nulliparous women with normal pregnancies were randomised to: 40 min of MA or EA, or SC without acupuncture. Questionnaires were administered the day after partus and 2 months later. RESULTS: Two months postpartum, the mean recalled pain on the visual analogue scale (SC: 70.1, MA: 69.3 and EA: 68.7) did not differ between the groups (SC vs MA: adjusted mean difference 0.8, 95 % confidence interval [CI] -6.3 to 7.9 and SC vs EA: mean difference 1.3 CI 95 % -5.5 to 8.1). Positive birth experience (SC: 54.3 %, MA: 64.6 % and EA: 61.0 %) did not differ between the groups (SC vs MA: adjusted Odds Ratio [OR] 1.8, CI 95 % 0.9 to 3.7 and SC vs EA: OR 1.4 CI 95 % 0.7 to 2.6). CONCLUSIONS: Despite the lower use of other pain relief, women who received acupuncture with the combination of manual and electrical stimulation during labour made the same retrospective assessments of labour pain and birth experience 2 months postpartum as those who received acupuncture with manual stimulation or standard care. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01197950.

Initiation into engineering : stability and change in gender orders

This article examines processes of doing gender during the initiation of students into engineering programs at university level in Sweden. The article draws on interviews with students, focusing on their understandings of gender. The aim is to explore difficulties with and challenges to traditional gender roles in an academic male dominated arena, by using theories of doing and undoing gender. The empirical material reveals the initiation period or ‘reception’ as a phenomenon both reinforcing and challenging traditional orders. The attempts to challenge norms meet resistance, revealing two paradoxes and one dilemma. In the first paradox the formal purpose of the reception (inclusion) is partly at odds with its informal consequence (exclusion of deviations). The second paradox concerns the contradictory effects of the reception. Even though the reception ensures participation of women, it reinforces existing hierarchies including gender inequality. This results in a dilemma, since in order to protect individual safety, there is a taboo on harassing women which then reproduces stable gender stereotypes. So while harassment taints the respect senior students must earn during the reception, the fact that female students exist in the engineering field challenges the established order and opens the way for change.

Multi-agent simulation of sawmill yard operations

This paper reports the findings of using multi-agent based simulation model to evaluate the sawmill yard operations within a large privately owned sawmill in Sweden, Bergkvist Insjön AB in the current case. Conventional working routines within sawmill yard threaten the overall efficiency and thereby limit the profit margin of sawmill. Deploying dynamic work routines within the sawmill yard is not readily feasible in real time, so discrete event simulation model has been investigated to be able to report optimal work order depending on the situations. Preliminary investigations indicate that the results achieved by simulation model are promising. It is expected that the results achieved in the current case will support Bergkvist-Insjön AB in making optimal decisions by deploying efficient work order in sawmill yard.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

Demonstration of Solar Heating and Cooling System using Sorption Integrated Solar Thermal Collectors

Producing cost-competitive small and medium-sized solar cooling systems is currently a significant challenge. Due to system complexity, extensive engineering, design and equipment costs; the installation costs of solar thermal cooling systems are prohibitively high. In efforts to overcome these limitations, a novel sorption heat pump module has been developed and directly integrated into a solar thermal collector. The module comprises a fully encapsulated sorption tube containing hygroscopic salt sorbent and water as a refrigerant, sealed under vacuum with no moving parts. A 5.6m2 aperture area outdoor laboratory-scale system of sorption module integrated solar collectors was installed in Stockholm, Sweden and evaluated under constant re-cooling and chilled fluid return temperatures in order to assess collector performance. Measured average solar cooling COP was 0.19 with average cooling powers between 120 and 200 Wm-2 collector aperture area. It was observed that average collector cooling power is constant at daily insolation levels above 3.6 kWhm-2 with the cooling energy produced being proportional to solar insolation. For full evaluation of an integrated sorption collector solar heating and cooling system, under the umbrella of a European Union project for technological innovation, a 180 m2 large-scale demonstration system has been installed in Karlstad, Sweden. Results from the installation commissioned in summer 2014 with non-optimised control strategies showed average electrical COP of 10.6 and average cooling powers between 140 and 250 Wm-2 collector aperture area. Optimisation of control strategies, heat transfer fluid flows through the collectors and electrical COP will be carried out in autumn 2014.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.