Review

Analysis and prediction of railway accident risks using machine learning

  • The harmful consequences of rail accidents, which sometimes lead to loss of life and the destruction of the system and its environment, are at the basis of the implementation of a "experience feedback" (REX) system considered as the essential means to promote the improvement of safety. REX seeks to identify adverse events with a view to highlighting all the causes that contributed to the occurrence of a particular accident and therefore to avoid at least the reproduction of new accidents and similar incidents. Accident and incident investigation reports provide a wealth of informative information for accident prevention. It would be appropriate to exploit these reports in order to extract the relevant information and suggest ways to avoid the reproduction of adverse events. In this context, knowledge of the causes of accidents results mainly from the contribution of lessons learned and experiences gained, whether positive or negative. However, the exploitation of information and the search for lessons from past events is a crucial step in the REX process. This process of analyzing and using data from experience can be facilitated if there are methods and tools available to technical investigators. It seems advisable to consider the use of artificial intelligence (AI) techniques and in particular automatic learning methods in order to understand the origins and circumstances of accidents and therefore propose solutions to avoid the reproduction of similar insecurity events. The fact that the lessons one can learn from a REX depends on the experiences of the situations experienced in the past, constitutes in itself a key argument in favor of machine learning. Thus, the identification of knowledge about rail accidents and incidents and share them among REX actors constitute a process of learning sequences of undesirable events. The approach proposed in this manuscript for the prevention of railway accidents is a hybrid method built around several algorithms and uses several methods of automatic learning: Learning by classification of concepts, Rule-based machine learning (RBML) and Case-based reasoning (CBR).

    Citation: Habib Hadj-Mabrouk. Analysis and prediction of railway accident risks using machine learning[J]. AIMS Electronics and Electrical Engineering, 2020, 4(1): 19-46. doi: 10.3934/ElectrEng.2020.1.19

    Related Papers:

    [1] Andrea Ehrmann, Tomasz Blachowicz . Recent coating materials for textile-based solar cells. AIMS Materials Science, 2019, 6(2): 234-251. doi: 10.3934/matersci.2019.2.234
    [2] Katrin Gossen, Marius Dotter, Bennet Brockhagen, Jan Lukas Storck, Andrea Ehrmann . Long-term investigation of unsealed DSSCs with glycerol-based electrolytes of different compositions. AIMS Materials Science, 2022, 9(2): 283-296. doi: 10.3934/matersci.2022017
    [3] Roger Chang, Kemakorn Ithisuphalap, Ilona Kretzschmar . Impact of particle shape on electron transport and lifetime in zinc oxide nanorod-based dye-sensitized solar cells. AIMS Materials Science, 2016, 3(1): 51-65. doi: 10.3934/matersci.2016.1.51
    [4] Ha Thanh Tung, Ho Kim Dan, Dang Huu Phuc . Effect of the calcination temperature of the FTO/PbS cathode on the performance of a quantum dot-sensitized solar cell. AIMS Materials Science, 2023, 10(3): 426-436. doi: 10.3934/matersci.2023023
    [5] Avner Neubauer, Shira Yochelis, Gur Mittelman, Ido Eisenberg, Yossi Paltiel . Simple down conversion nano-crystal coatings for enhancing Silicon-solar cells efficiency. AIMS Materials Science, 2016, 3(3): 1256-1265. doi: 10.3934/matersci.2016.3.1256
    [6] Silvia Colodrero . Conjugated polymers as functional hole selective layers in efficient metal halide perovskite solar cells. AIMS Materials Science, 2017, 4(4): 956-969. doi: 10.3934/matersci.2017.4.956
    [7] Etsana Kiros Ashebir, Berhe Tadese Abay, Taame Abraha Berhe . Sustainable A2BBX6 based lead free perovskite solar cells: The challenges and research roadmap for power conversion efficiency improvement. AIMS Materials Science, 2024, 11(4): 712-759. doi: 10.3934/matersci.2024036
    [8] Carlos N. Kabengele, Giresse N. Kasiama, Etienne M. Ngoyi, Clement L. Inkoto, Juvenal M. Bete, Philippe B. Babady, Damien S. T. Tshibangu, Dorothée D. Tshilanda, Hercule M. Kalele, Pius T. Mpiana, Koto-Te-Nyiwa Ngbolua . Biogenic synthesis, characterization and effects of Mn-CuO composite nanocatalysts on Methylene blue photodegradation and Human erythrocytes. AIMS Materials Science, 2023, 10(2): 356-369. doi: 10.3934/matersci.2023019
    [9] Stavroula Sfaelou, Panagiotis Lianos . Photoactivated Fuel Cells (PhotoFuelCells). An alternative source of renewable energy with environmental benefits. AIMS Materials Science, 2016, 3(1): 270-288. doi: 10.3934/matersci.2016.1.270
    [10] Takayuki Aoyama, Mari Aoki, Isao Sumita, Atsushi Ogura . Effects of particle size of aluminum powder in silver/aluminum paste on n-type solar cells. AIMS Materials Science, 2018, 5(4): 614-623. doi: 10.3934/matersci.2018.4.614
  • The harmful consequences of rail accidents, which sometimes lead to loss of life and the destruction of the system and its environment, are at the basis of the implementation of a "experience feedback" (REX) system considered as the essential means to promote the improvement of safety. REX seeks to identify adverse events with a view to highlighting all the causes that contributed to the occurrence of a particular accident and therefore to avoid at least the reproduction of new accidents and similar incidents. Accident and incident investigation reports provide a wealth of informative information for accident prevention. It would be appropriate to exploit these reports in order to extract the relevant information and suggest ways to avoid the reproduction of adverse events. In this context, knowledge of the causes of accidents results mainly from the contribution of lessons learned and experiences gained, whether positive or negative. However, the exploitation of information and the search for lessons from past events is a crucial step in the REX process. This process of analyzing and using data from experience can be facilitated if there are methods and tools available to technical investigators. It seems advisable to consider the use of artificial intelligence (AI) techniques and in particular automatic learning methods in order to understand the origins and circumstances of accidents and therefore propose solutions to avoid the reproduction of similar insecurity events. The fact that the lessons one can learn from a REX depends on the experiences of the situations experienced in the past, constitutes in itself a key argument in favor of machine learning. Thus, the identification of knowledge about rail accidents and incidents and share them among REX actors constitute a process of learning sequences of undesirable events. The approach proposed in this manuscript for the prevention of railway accidents is a hybrid method built around several algorithms and uses several methods of automatic learning: Learning by classification of concepts, Rule-based machine learning (RBML) and Case-based reasoning (CBR).


    1. Introduction

    Solar power is the largest renewable energy resource, and the demand for higher solar power capacity is increasing every year. In urban areas, considerable surface areas of the sides and windows of buildings are not utilized for light absorption. These surface areas can be covered with solar cells that are completely transparent and colorless at night and function similar to photochromic sunglasses, to provide for the best view. The cells act as window shades during the day by tinting the glass and absorbing a section of the visible range of sunlight to generate electric power.

    There are a few classes of organic compounds that exhibit this photochromic property—diarylethenes [1,2,3,4], azas [5], spiropyrans [6,7,8], and spirooxazines [9,10]. Spiropyran and its derivatives with the spirooxazines are among the most studied organic compounds in this group, with high stability and broad visible light absorption, so these compounds were chosen for study in this work.

    Both spiropyrans and spirooxazines have the same mechanism of color change. When in the dark, these compounds have a closed ring conformation, which breaks into an extended chain conformation when exposed to UV light. In the closed spiro form, the two conjugated systems are separated by an sp3-hybridized carbon. Once opened into the merocyanine form, they are connected to form an extended π system, allowing for the absorption of visible light. They can then relax back into the closed ring structure with time and ambient heat (Scheme 1) [11]. The rate of this transformation is retarded when the compounds are trapped in a rigid solid matrix such as a polymer system. However, the transformation occurs when the compounds are provided with physical space for their molecular expansion [10].

    Scheme 1. Mechanism of spiropyran/spirooxazine color change.

    With this is mind, we absorbed spiropyrans and spirooxazines onto a thin and transparent mesoporous titanium dioxide photoanode, to act as the sensitizer in a dye-sensitized solar cell (DSSC) [12]. Originally discovered in 1991 by Grätzel, DSSCs have exhibited remarkable robustness, with a wide variety of sensitizers, substrates, electrolytes, and electrodes [13,14,15,16]. Organometallic-based dyes, such as the ruthenium-based N719 dye, have been used widely [17], as well as many organic cells that avoid the use of rare earth metals [18,19,20].

    In this work, we report the fabrication and characterization of DSSCs, with the sensitizing unit being composed of a spiropyran moiety. This cell exhibits the ability to change color depending on ambient light conditions, and therefore it has the potential to be used as a solar cell to be installed on windows. In the process of preparing this publication, we noticed the recent publication of a DSSC based on another photochromic compound with a similar concept, so the references are cited here [21,22].

    2. Materials and Method

    The solar cells were prepared according to the procedure described previously [23]. A titanium dioxide paste was purchased from Solaronix (Ti-Nanoxide T/SP). The paste was then doctor bladed onto FTO glass (Hartford Glass, 7 Ω/γ) and sintered at 500 °C for 1 h and cooled to 80 °C, at which point it was immersed in the respective dye solutions (0.5 mM in methanol) for 48 h. Next, the photoanode was rinsed with ethanol and dried. The cell was completed by sandwiching an electrolyte solution consisting of 0.5 M lithium iodide (Aldrich 99.9%) and 0.05 M iodine (Aldrich 99%) in an acetonitrile together with the photoanode and acounter electrode. The cathode was prepared by spin coating 50 µL of a 5 mM solution consisting of chloroplatinic acid hydrate (Aldrich, 99.9%) in 2-propanol (Aldrich, 99%) onto FTO glass (Hartford Glass, 15 Ω/γ) and annealing at 400 °C for 40 min. The cells were assembled using standard published procedures [24]. The photoanode thickness, determined by SEM was 13 µm (see Supporting Information). The solar cells were tested with a Gamry Reference 600 potentiostat, using a 300 W Xe lamp and 0.25 cm2 mask, filtered to 1.5 AM (100 mW cm−2). It should be noted that the efficiencies of the DSSCs were lower than what is reported for state-of-the-art DSSC and there are several reason. Firstly, our DSSCs contain no additives to boost performance. For instance, with the addition of 4-tert-butylpyridine (TBP), previous work has shown that DSSCs achieve efficiencies of 8.5% but without TBP cell efficiency drops to 3.7% [25]. Secondly, our fabricated DSSCs did not contain a compact TiO2 layer in between the photoanode and FTO, which is known to improve performance by reducing the number of reaction sites for the recombination of triiodide with electrons on the bare FTO. Adding a compact layer has been shown to improve efficiency by up to 33.3% [26]. Third, they did not contain a scattering layer, which can enhance the efficiency by up to 30% [27], mainly by improving light harvest efficiency, thereby improving . Fourth, the DSSC here did not undergo a TiCl4 post-treatment of the TiO2 layer, which normally improves the current density by roughly 18% without affecting the open-circuit voltage [28]. The additives and other treatments are optimized for ruthenium based DSSCs and not for this novel class of photochromic dyes. By not including these enhancements, we are isolating and only comparing the effect of the dye molecule on the performance of the DSSC without any unwanted interference.

    The first attempted photochromic solar cell was made using the compound known as “Oxford Blue, ” (1) shown in Figure 1. This spirooxazine is a product of Keystone Inc. (Chicago, IL), and can be prepared according to an existing procedure [29].

    Figure 1. Left: 1-isobutyl-3,3-dimethylspiro[indoline-2,3'-naphtho[2,1-b] [1,4]oxazine] (1); Right: 3-(3',3'-dimethyl-6-nitrospiro[chromene-2,2'-indolin]-1'-yl)propanoic acid (2).

    In our first approach, the TiO2 layer on the FTO anode on the glass was soaked in a dye solution of CH3CN for 48 h. Then the glass was rinsed with methanol twice and dried before a measurement was made. As is seen in Figure 1, the photocurrent is very small at around 0.08 mA/cm2. Diffusion of 1 due to the lack of an anchoring group to bind to the TiO2 photoanode is a likely cause for the low current. We believe that most of the dye was rinsed off the TiO2 surface and therefore, little photocurrent was generated when the device was exposed to sunlight.

    The lack of an anchoring group necessitated the use of a spray-coated epoxy polymer layer to minimize molecular diffusion [30]. In a modified approach, the dye solution was dropped on the TiO2 layer and dried after the solvent was evaporated. A thin, uniform layer of the dye was noticeable on the TiO2 that changed to a blue color when the coating is exposed to sunlight (Figure 2). To avoid the diffusion of the dye into the electrolyte solution, a thin layer of epoxy polymer in ether was spray coated on the top of the dye film. After the epoxy layer was solidified, a DSSC device was prepared for photocurrent test. The results were similar to those without using the epoxy coating. We believe that the epoxy coating limited mass transport of the triiodide/iodide redox couple and the dye was unable to regenerate itself. Furthermore, the low efficiency is expected because the epoxy layer cannot completely block the diffusion of the dye into the electrolyte. This was evidenced from the gradual color change of the electrolytes and the loss of the capability to generate electricity of the cell. Therefore, modifications were deemed necessary to improve efficiency. The main issue seen before was absorption to the surface, so a modified form was chosen with carboxylic acid functionality that can adhere to the titanium dioxide substrate (Figure 1). Therefore, compound 2 was synthesized according to an existing procedure [11,31].

    Figure 2. Color change of the dye on the TiO2 film before (left) and after (right) exposure to sunlight.

    The solar cell using dye (2) was transparent through most of the visible region, which was the goal of this overall project (Figure 2). The solar cell based on dye (2) showed a significant improvement with a conversion efficiency (Table 1) of 0.028%, 4 times greater than dye (1), with the reference cell using N-719 [32] achieving a conversion efficiency of 1.9% under the same conditions (Figure 3). We were unable to do IPCE of dye (1) and (2) due to the low current values. This indicates a usable efficiency from the carboxylic attachment to the titanium dioxide substrate. As a comparison, we fabricated a dye-free, bare TiO2 DSSC which showed no clear J-V behavior (Figure 4s).

    Table 1. Solar cell performance.
    DyeJsc (mA/cm2)Voc (mV)Fill Factor (%)ɳ (%)
    N7194.12760631.9
    (1)0.08208410.007
    (2)0.1049055.90.028
     | Show Table
    DownLoad: CSV
    Figure 3. (A) J-V behaviour for DSSCs fabricated using N719 (red), dye 1 (green), dye 2 (blue). (B) UV-vis absorption comparing dye 2 and a reference N719 dye solution.

    Although the efficiency is much improved, it is still very low. Most of the losses come from a reduction in the short circuit current and a decrease in the open circuit voltage. The low photocurrent can be partially attributed to a low absorption of solar radiation as seen in Figure 3. It is also possible that the HOMO and LUMO of the dyes are not well aligned with the conduction band of TiO2 and the redox potential of iodide/triiodide. Poor alignment would make charge injection inefficient and also limit dye regeneration. Furthermore, it is known that the open-circuit voltage is determined by the difference between the redox potential and the TiO2 fermi energy level. Since not many electrons are generated, the fermi energy level is closer to the valence band rather than the conduction band of TiO2. This decreases the energy difference between the fermi level and the redox potential and can be a possible reason for the lower open-circuit voltage seen in dyes (1) and (2).

    3. Conclusion

    We have successfully synthesized and assembled photochromic dye-sensitized solar cells using a spiropyran moiety as the light-sensitizing compound. This compound showed color changes which allow for diurnal variability, making it potentially useful as a solar panel for windows. We believe that the photochromic dyes can be modified to obtain a higher photocurrent by taking better advantage of the solar spectrum and by more effectively injecting excited electrons into the TiO2 photoanode.

    Conflict of Interest

    The authors declare no conflict of interest in this paper.



    [1] Hadj-Mabrouk H (2018) New approach of assessing human errors in railways. Transactions of the VSB - Technical University of Ostrava, Safety Engineering Series 13: 1-17.
    [2] Hadj-Mabrouk H (2019) Consideration of Human Factors in the Accident and Incident Investigation Process. Application to the Safety of Railway Transport. J Ergon Adv Res 1: 1-20.
    [3] Hadj-Mabrouk H (2016) Knowledge based system for the evaluation of safety and the prevention of railway accidents. International journal of railway 3: 37-44.
    [4] Bergmeir C, Sáinz G, Bertrand CM, et al. (2013) A Study on the Use of Machine Learning Methods for Incidence Prediction in High-Speed Train Tracks. IEA/AIE 2013 Proceedings of the 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems 7906: 674-683.
    [5] Fay A (2000) A fuzzy knowledge-based system for railway traffic control. Eng Appl Artif Intel 13: 719-729. doi: 10.1016/S0952-1976(00)00027-0
    [6] Santur Y, Karaköse M, Akin E (2017) A new rail inspection method based on deep learning using laser cameras. International Artificial Intelligence and Data Processing Symposium (IDAP) 16-17.
    [7] Faghih-Roohi S, Hajizadeh S, Núñez A, et al. (2016) Deep convolutional neural networks for detection of rail surface defects. International Joint Conference on Neural Networks (IJCNN) 24-29.
    [8] Ghofrania F, He Q, Goverde R, et al. (2018) Recent applications of big data analytics in railway transportation systems: A survey. Transport Res C-Emer 90: 226-246. doi: 10.1016/j.trc.2018.03.010
    [9] Thaduri A, Galar D, Kumar U (2015) Railway assets: A potential domain for big data analytics. Procedia Comput Sci 53: 457-467. doi: 10.1016/j.procs.2015.07.323
    [10] Attoh-Okine N (2014) Big data challenges in railway engineering. IEEE International Conference on Big Data (Big Data) 27-30.
    [11] Hughes P (2018) Making the railway safer with big data. Available from: http://www.railtechnologymagazine.com/Comment/making-the-railway-safer-with-big-data.
    [12] Hayward V (2018) Big data & the Digital Railway. Available from: https://on-trac.co.uk/big-data-digital-railway/.
    [13] Marr B (2017) How Siemens Is Using Big Data And IoT To Build The Internet Of Trains. Available from: https://www.forbes.com/sites/bernardmarr/2017/05/30/how-siemens-is-using-big-data-and-iot-to-build-the-internet-of-trains/#2b7a4b6e72b8.
    [14] Williams T, Betak J, Findley B (2016) Text Mining Analysis of Railroad Accident Investigation Reports. Proceedings of the 2016 Joint Rail Conference.
    [15] Brown DE (2016) Text Mining the Contributors to Rail Accidents. IEEE Transactions on Intelligent Transportation Systems 17: 346-355. doi: 10.1109/TITS.2015.2472580
    [16] Li J, Wang J, Xu N, et al. (2018) Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method. Information-an International Interdisciplinary Journal 9: 26
    [17] Williams T, Betakbc J (2018) A Comparison of LSA and LDA for the Analysis of Railroad Accident Text. Procedia Computer Science 130: 98-102. doi: 10.1016/j.procs.2018.04.017
    [18] Syeda K, Shirazi SN, Naqvi SA, et al. (2018) Big Data and Natural Language Processing for Analysing Railway Safety: Analysis of Railway Incident Reports. Innovative Applications of Big Data in the Railway Industry 240-267.
    [19] Van-Gulijk C, Hughes P, Figueres-Esteban M, et al. (2018) The case for IT transformation and big data for safety risk management on the GB railways. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232: 151-163. doi: 10.1177/1748006X17728210
    [20] Syeda KN, Shirazi SN, Naqvi SAA, et al. (2017) Big Data and Natural Language Processing for Analysing Railway Safety. Innovative Applications of Big Data in the Railway Industry. IGI Global Publishing 240-267.
    [21] Ghomi H, Bagheri M, Fu L, et al (2016) Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study. Traffic Injury Prevention 17: 833-841. doi: 10.1080/15389588.2016.1151011
    [22] Zhang X, Green E, Chen M, et al. (2019) Identifying secondary crashes using text mining techniques. Journal of Transportation Safety & Security 1-21.
    [23] Heidarysafa M, Kowsari K, Barnes LE, et al. (2018) Analysis of Railway Accidents' Narratives Using Deep Learning. International Conference on Machine Learning and Applications (LCMLA) 1446-1453.
    [24] Gibert X, Patel VM, Chellappa R (2017) Deep multitask learning for railway track inspection. IEEE T Intell Transp 18: 153-164. doi: 10.1109/TITS.2016.2568758
    [25] Osman A, Hajij M, Bakhit PR, et al. (2019) Prediction of Near-Crashes from Observed Vehicle Kinematics Using Machine Learning. Transportation Res Rec.
    [26] Nakhaee MC, Hiemstra D, Stoelinga M, et al. (2019) The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. In: Collart-Dutilleul S., Lecomte T., Romanovsky A. (eds) Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. RSSRail 2019. Lecture Notes in Computer Science.
    [27] Zubair M, Khan MJ, Awais M (2012) Prediction and analysis of air incidents and accidents using case-based reasoning. Third Global Congress on Intelligent Systems 315-318.
    [28] Khattak A, Kanafani A (1996) Case-based reasoning: A planning tool for intelligent transportation systems. Transport Res C-Emer 4: 267-288. doi: 10.1016/S0968-090X(97)82901-4
    [29] Sadeka A, Smith B, Demetsky M (2001) A prototype case-based reasoning system for real-time freeway traffic routing. Transport Res C-Emer 9: 353-380. doi: 10.1016/S0968-090X(00)00046-2
    [30] Sadek A, Demetsky M, Smith B (1999) Case-Based Reasoning for Real-Time Traffic Flow Management. Comput-Aided Civ Inf 14:347-356. doi: 10.1111/0885-9507.00153
    [31] Zhenlong L, Xiaohua Z (2008) A case-based reasoning approach to urban intersection control. 7th World Congress on Intelligent Control and Automation 7113-7118.
    [32] Li K, Waters NM (2005) Transportation Networks, Case-Based Reasoning and Traffic Collision Analysis: A Methodology for the 21st Century. In: Reggiani A, Schintler LA (eds.), Methods and Models in Transport and Telecommunications, 63-92.
    [33] Kofod-Petersen A, Andersen OJ, Aamodt A (2014) Case-Based Reasoning for Improving Traffic Flow in Urban Intersections. International Conference on Case-Based Reasoning 8765: 215-229.
    [34] Louati A, Elkosantini S, Darmoul S, et al. (2016) A case-based reasoning system to control traffic at signalized intersections. IFAC-Papers On Line 49: 149-154.
    [35] Begum S, Ahmed MU, Funk P, et al. (2012) Mental state monitoring system for the professional drivers based on Heart Rate Variability analysis and Case-Based Reasoning. Federated Conference on Computer Science and Information Systems (FedCSIS) 35-42.
    [36] Zhong Q, Zhang G (2017) A Case-Based Approach for Modelling the Risk of Driver Fatigue. International Conference on Intelligence Science 510: 45-56.
    [37] Varma A, Roddy N (1999) ICARUS: Design and deployment of a case-based reasoning system for locomotive diagnostics. Eng Appl Artif Intel 12: 681-690. doi: 10.1016/S0952-1976(99)00039-1
    [38] Johnson C (2000) Using case-based reasoning to support the indexing and retrieval of incident reports. Proceeding of European Safety and Reliability Conference (ESREL 2000): Foresight and Precaution, Balkema, Rotterdam, the Netherlands 1387-1394.
    [39] Cui Y, Tang Z, Dai H (2005) Case-based reasoning and rule-based reasoning for railway incidents prevention. Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management 2: 1057-1060.
    [40] Li X, Yu K (2010) The research of intelligent Decision Support system based on Case-based Reasoning in the Railway Rescue Command System. International Conference on Intelligent Control and Information Processing 59-63.
    [41] Lu Y, Li Q, Xiao W (2013) Case-based reasoning for automated safety risk analysis on subway operation: Case representation and retrieval. Safety Sci 57: 75-81. doi: 10.1016/j.ssci.2013.01.020
    [42] de Souza VDM, Borges AP, Sato DMV, et al. (2016) Automatic knowledge learning using Case-Based Reasoning: A case study approach to automatic train conduction. International Joint Conference on Neural Networks (IJCNN) 4579-4585.
    [43] Zhao H, Chen H, Dong W, et al. (2017) Fault diagnosis of rail turnout system based on case-based reasoning with compound distance methods. 29th Chinese Control And Decision Conference (CCDC) 4205-4210.
    [44] Hadj-Mabrouk H (2017) Preliminary Hazard Analysis (PHA): New hybrid approach to railway risk analysis. International Refereed Journal of Engineering and Science 6: 51-58.
    [45] Hadj-Mabrouk H (2016) Machine learning from experience feedback on accidents in transport. 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications 246-251.
    [46] Ganascia JG (1987) Agape et Charade: deux mécanismes d'apprentissage symbolique appliqués à la construction de bases de connaissances. Thèse d'État, Université Paris-sud, France.
    [47] Quinlan JR (1986) Induction of Decision Trees. Mach Learn 1: 81-106.
    [48] Hadj-Mabrouk H (2016) CLASCA: Learning System for Classification and Capitalization of Accident Scenarios of Railway. Journal of Engineering Research and Application 6: 91-98.
    [49] Hadj-Mabrouk H (2018) A Hybrid Approach for the Prevention of Railway Accidents Based on Artificial Intelligence. International Conference on Intelligent Computing & Optimization 383-394.
    [50] Hadj-Mabrouk H (2019) Contribution of artificial intelligence to risk assessment of railway accidents. Journal of Urban Rail Transit 5: 104-122. doi: 10.1007/s40864-019-0102-3
    [51] Hadj-Mabrouk H, Mejri H (2015) ACASYA: a knowledge-based system for aid in the storage, classification, assessment and generation of accident scenarios. Application to the safety of rail transport systems. Advances in Computer Science an International Journal 4: 7-13.
    [52] Hadj-Mabrouk H (2017) Contribution of learning Charade system of rules for the prevention of rail accidents. Intell Decis Technol 11: 477-485. doi: 10.3233/IDT-170304
    [53] Aamodt A, Plaza E (1994) Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun 7: 39-52.
    [54] Harmon P (1991) Case-based reasoning II. Intelligent Software Strategies 7: 1-9.
    [55] Kolodner J (1992) An introduction to case-based reasoning. Artif Intell Rev 6: 3-34. doi: 10.1007/BF00155578
    [56] Leake D (1996) CBR in Context: The present and future. Case-Based Reasoning: Experiences, Lessons, and Future Directions 3-30.
    [57] Mott S (1993) Case-based reasoning: Market, applications, and fit with other technologies. Expert Syst Appl 6: 97-104. doi: 10.1016/0957-4174(93)90022-X
    [58] Pinson S, Demourioux M, Laasri B, et al. (1993) Le Raisonnement à Partir de Cas: panorama et modélisation dynamique. Séminaire CBR, LAFORIA, Rapport 93/42, 1er octobre.
    [59] Slade S (1991) Case-based reasoning: A research paradigm. AI Mag 12: 42-55.
    [60] Hadj-Mabrouk H (2017) Case-Based Reasoning for the Evaluation of Safety Critical Software. Application to The Safety of Railway Transport. International Journal of Engineering Research and Development 13: 37-43.
    [61] Hadj-Mabrouk H (2019) Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport. AIMS Electronics and Electrical Engineering 3: 33-70. doi: 10.3934/ElectrEng.2019.1.33
    [62] Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27: 379-423. doi: 10.1002/j.1538-7305.1948.tb01338.x
  • This article has been cited by:

    1. Yuriy Y. Smolin, Sruthi Janakiraman, Masoud Soroush, Kenneth K.S. Lau, Experimental and theoretical investigation of dye sensitized solar cells integrated with crosslinked poly(vinylpyrrolidone) polymer electrolyte using initiated chemical vapor deposition, 2017, 635, 00406090, 9, 10.1016/j.tsf.2016.12.034
    2. Aishling Dunne, Colm Delaney, Aoife McKeon, Pavel Nesterenko, Brett Paull, Fernando Benito-Lopez, Dermot Diamond, Larisa Florea, Micro-Capillary Coatings Based on Spiropyran Polymeric Brushes for Metal Ion Binding, Detection, and Release in Continuous Flow, 2018, 18, 1424-8220, 1083, 10.3390/s18041083
    3. Yuriy Y. Smolin, Kenneth K. S. Lau, Masoud Soroush, First‐principles modeling for optimal design, operation, and integration of energy conversion and storage systems, 2019, 65, 0001-1541, e16482, 10.1002/aic.16482
    4. Noah M. Johnson, Yuriy Y. Smolin, Daniel Hagaman, Masoud Soroush, Kenneth K. S. Lau, Hai-Feng Ji, Suitability of N-propanoic acid spiropyrans and spirooxazines for use as sensitizing dyes in dye-sensitized solar cells, 2017, 19, 1463-9076, 2981, 10.1039/C6CP07853B
    5. Dipak Kumar Baisnab, Supratim Mukherjee, Soumen Das, 2021, 9780128197189, 231, 10.1016/B978-0-12-819718-9.00007-8
    6. Bart W. H. Saes, Martijn M. Wienk, René A. J. Janssen, Photochromic organic solar cells based on diarylethenes, 2020, 10, 2046-2069, 30176, 10.1039/D0RA04508J
    7. Ana Flavia Nogueira, Shades of transparency, 2020, 5, 2058-7546, 428, 10.1038/s41560-020-0639-0
    8. Quentin Huaulmé, Valid M. Mwalukuku, Damien Joly, Johan Liotier, Yann Kervella, Pascale Maldivi, Stéphanie Narbey, Frédéric Oswald, Antonio J. Riquelme, Juan Antonio Anta, Renaud Demadrille, Photochromic dye-sensitized solar cells with light-driven adjustable optical transmission and power conversion efficiency, 2020, 5, 2058-7546, 468, 10.1038/s41560-020-0624-7
    9. José-María Andrés Castán, Valid Mwatati Mwalukuku, Antonio J. Riquelme, Johan Liotier, Quentin Huaulmé, Juan A. Anta, Pascale Maldivi, Renaud Demadrille, Photochromic spiro-indoline naphthoxazines and naphthopyrans in dye-sensitized solar cells, 2022, 6, 2052-1537, 2994, 10.1039/D2QM00375A
    10. Masoud Soroush, Yashar Hajimolana, Sunlight harvesting, 2023, 170, 00981354, 108103, 10.1016/j.compchemeng.2022.108103
    11. Vincent M. Wheeler, Janghyun Kim, Tom Daligault, Bryan A. Rosales, Chaiwat Engtrakul, Robert C. Tenent, Lance M. Wheeler, Photovoltaic windows cut energy use and CO2 emissions by 40% in highly glazed buildings, 2022, 5, 25903322, 1271, 10.1016/j.oneear.2022.10.014
    12. Samuel Fauvel, Antonio J. Riquelme, José-María Andrés Castán, Valid Mwatati Mwalukuku, Yann Kervella, Vijay Kumar Challuri, Frédéric Sauvage, Stéphanie Narbey, Pascale Maldivi, Cyril Aumaître, Renaud Demadrille, Push-pull photochromic dyes for semi-transparent solar cells with light-adjustable optical properties and high color-rendering index, 2023, 14, 2041-6520, 8497, 10.1039/D3SC02328A
    13. Josephine L. Surel, Jeffrey A. Christians, Can we make color switchable photovoltaic windows?, 2023, 14, 2041-6520, 7828, 10.1039/D3SC01811C
    14. A. D. Pugachev, I. A. Rostovtseva, N. I. Makarova, M. Yu. Ievlev, V. S. Dmitriev, I. V. Ozhogin, V. V. Tkachev, A. N. Utenyshev, I. G. Borodkina, A. V. Metelitsa, S. M. Aldoshin, V. I. Minkin, B. S. Luk’yanov, Synthesis and study of new photochromic halogen-substituted spiropyrans of the indoline series, 2023, 72, 1066-5285, 2637, 10.1007/s11172-023-4068-7
    15. Zibo Zhou, Wei Shao, Qinan Wang, Qianqing Jiang, Dianyi Liu, Solid‐State Photochromic Transparent Photovoltaics with Bisthienylethene‐Based Molecules, 2024, 2367-198X, 10.1002/solr.202400725
    16. Weifan Luo, José María Andrés Castán, Diego Mirani, Antonio J. Riquelme, Amit Kumar Sachan, Olzhas Kurman, SunJu Kim, Fabiola Faini, Paul Zimmermann, Alexander Hinderhofer, Yash Patel, Aaron T. Frei, Jacques‐E. Moser, Daniel Ramirez, Frank Schreiber, Pascale Maldivi, Ji‐Youn Seo, Wolfgang Tress, Giulia Grancini, Renaud Demadrille, Jovana V. Milić, Photochromic Control in Hybrid Perovskite Photovoltaics, 2025, 0935-9648, 10.1002/adma.202420143
    17. Vladimir B. Motalov, Anatoliy M. Dunaev, Andrey S. Kaplin, Lev S. Kudin, Artem D. Pugachev, Gennady S. Borodkin, Ilya V. Ozhogin, Thermodynamic properties of indoline-based spiropyrans: effect of substituents in positions 6’ and 8’ on sublimation enthalpy, 2025, 1388-6150, 10.1007/s10973-025-14382-7
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(8174) PDF downloads(980) Cited by(11)

Article outline

Figures and Tables

Figures(4)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog