Sharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Twinner: A framework for automated software deobfuscation348535092160110.24200/sci.2019.21601ENB. MomeniDepartment of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155/1639, IranM. KharraziDepartment of Computer Engineering, Sharif University of Technology, Tehran, P.O. Box 11155/1639, IranJournal Article20170729Malware analysis is essential to understanding the internal logic and intent of malware programs in order to mitigate their threats. As the analysis methods have evolved, malware authors have adopted more techniques such as the virtualization obfuscation to protect the malware inner workings. This manuscript presents a framework for deobfuscating software which abstracts the input program as much as a mathematical model of its behavior, through monitoring every single operation performed during the malware execution. Also<br />the program is guided to run through its dierent execution paths automatically in order to gather as much knowledge as possible in the shortest time span. This makes it possible to nd hidden logics and deobfuscate dierent obfuscation techniques without being dependent on their specic details. The resulting model is then recoded as a C program without the articially added complexities. This code is called a twincode and behaves in the same manner as the obfuscated binary. As a proof of concept, the proposed framework is implemented and its eectiveness is evaluated on obfuscated binaries. Program control flow graphs are<br />inspected as a measure of successful code recovery. The performance of the proposed framework is evaluated using the set of SPEC test programs.https://scientiairanica.sharif.edu/article_21601_134cc81e656baa348a8043337c3ae92d.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Two sufficient conditions for the existence of path factors in graphs351035142101010.24200/sci.2018.5151.1122ENS. ZhouSchool of Science, Jiangsu University of Science and Technology
Mengxi Road 2, Zhenjiang, Jiangsu 212003, People&#039;s Republic of ChinaF. YangSchool of Science, Jiangsu University of Science and Technology, Mengxi Road 2, Zhenjiang, Jiangsu 212003, P. R. China.L. XuDepartment of Mathematics, Changji University, Changji, Xinjiang 831100, P. R. China.Journal Article20170909A graph G is called a (P≥n, k)-factor critical graph if G − U has a P≥ n -factor for any U ⊆ V(G) with|U|=k. A graphG is called a (P≥n, m)-factor deleted graph if.............https://scientiairanica.sharif.edu/article_21010_f105b9c43c729606aec01a75feb7596d.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Joint distribution adaptation via feature and model matching351535392114910.24200/sci.2018.5487.1304ENM. MardaniFaculty of IT & Computer Engineering, Urmia University of Technology, Urmia, Iran.J. TahmoresnezhadFaculty of IT & Computer Engineering, Urmia University of Technology, Urmia, Iran0000-0002-4893-1272Journal Article20171031It is usually supposed that the training (source domain) and test (target domain) data follow a similar distribution and feature space in most pattern recognition tasks. However, in many real-world applications, particularly in visual recognition, this hypothesis has been frequently violated. This problem is known as domain shift problem. Domain adaptation and transfer learning are promising techniques to learn an effective and robust classifier to tackle shift problem. In this paper, we propose a novel scheme for domain adaptation, entitled as Joint Distribution Adaptation via Feature and Model Matching (JDAFMM), in which feature transform and model matching are jointly optimized. Due to joint optimization, we can have a robust model with feasible feature transformation and model parameter adaptation. By introducing regularization operated between the marginal and conditional distributions’ shifts across domains, we can successfully adapt data drift as much as possible along with empirical risk minimization and rate of consistency maximization between manifold and prediction function. We conduct extensive experiments to evaluate the performance of the proposed model against those of other machine learning and domain adaptation methods in three types of visual benchmark datasets. Our experiments illustrate that our JDAFMM significantly outperforms other baseline and state-of-the-art methods.https://scientiairanica.sharif.edu/article_21149_3e3b9fd91ccd4df7f81c282a81533391.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201RUbIn: A framework for reliable and ubiquitous inference in WSNs354035552137310.24200/sci.2019.51495.2215ENA. ShamsaieDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran0000-0003-4548-0442J. HabibiDepartment of Computer Engineering, Sharif University of Technology, Tehran, IranE. AbdiDepartment of Computer Engineering, Sharif University of Technology, Tehran, IranF. GhassemiSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20180810Development of IoT applications brings a new movement to the functionality of wireless sensor networks (WSNs) from only environment sensing and data gathering to collaborative inferring and ubiquitous intelligence. In intelligent WSNs, nodes collaborate to exchange the information needed to achieve the required inference or smartness. Efficiency or correctness of many smart applications relies on the efficient, timely, reliable, and ubiquitous inference of information. In this paper, we introduce the RUbIn framework which provides a generic solution for such ubiquitous inferences. RUbIn brings the reliability and ubiquity for inferences using the redundancy characteristic of the gossiping protocols. With RUbIn, the implementation of such inferences and the control of their speed and cost is abstracted by providing developers with a proposed middleware and some dissemination control services.<br /><br />We develop a prototype implementation of the RUbIn framework and a few inference examples on TinyOS. For evaluation, we utilize both the TOSSIM simulator and a testbed of MicaZ motes in various densities and different number of nodes. Results of the evaluations demonstrate that in all nodes, the inferring time after a change is about a few seconds and the cost of maintenance in stability state is about a few sends per hour.https://scientiairanica.sharif.edu/article_21373_d0a7718e948f8a5143c696b436cae834.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Towards green data center microgrids by leveraging data center loads in providing frequency regulation355935702162010.24200/sci.2019.53454.3246ENW. QiDepartment of Electrical and Computer Engineering, Clarkson University, Potsdam, 13699, NY, USA.J. LiDepartment of Electrical and Computer Engineering, Clarkson University, Potsdam, 13699, NY, USA.Journal Article20190501In an electricity grid, imbalance between generation and load need be corrected within seconds so that frequency deviations will not threaten the stability and security. This is especially important for a low inertia microgrid operated in islanded mode, which is equipped with a limited number of synchronous generators in regulating frequency. To this end, for data center microgrid with limited on-site generators and increased green energy, when isolated from the utility grid, frequency deviation due to generation-load imbalance could be potentially corrected by conventional generating units as well as data center loads. Focusing on high PV penetrated data center microgrid operated in islanded mode, this paper explores effective control strategies for data center loads to participate in primary frequency response. By analyzing unique operational characteristics of traditional and PV generation units, uninterruptible power supply (UPS) units, and power consumption characteristics of IT components and cooling systems, the proposed load control strategy design effectively utilizes primary FR capabilities while not compromising data center quality of service (QoS) requirements. Numerical simulations via MATLAB/Simulink illustrate effectiveness of the proposed load control strategy in enhancing renewable energy penetration without compromising the system stability and security, providing a viable solution for future green data centers.https://scientiairanica.sharif.edu/article_21620_f8abd5bf7a8741a00d63b09e73bdf44d.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Risk-based cooperative scheduling of demand response and electric vehicle aggregators357135812155710.24200/sci.2019.53685.3446ENP. AliasgahriDepartment of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Blvd., Tabriz, IranB. Mohammadi-IvatlooDepartment of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Blvd., Tabriz, IranM. AbapourDepartment of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Blvd., Tabriz, IranJournal Article20190621This paper proposes a new cooperative scheduling framework for demand response aggregators (DRAs) and electric vehicle aggregators (EVAs) in a day-ahead market. The proposed model implements the information-gap decision theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees obtaining the predetermined profit by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenario-based and a bi-level IGDT based methods, respectively. The DR aggregator provides DR from two demand side management programs including time-of-use (TOU) and reward-based DR. Then, the obtained DR is offered into day-ahead markets. Furthermore, the EVA not only meet the EV owners’ demand economically, but also participates in the day-ahead mark while willing to set DR contracts with the DR aggregator. The objective function is to maximize the total profit of DR and EV aggregators perusing two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated on a case study with realistic data of electricity markets.https://scientiairanica.sharif.edu/article_21557_096c66d0c54677ba61c2ebae8f47a249.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Evaluation of online techniques utilized for extracting the transformer transfer function358235912154910.24200/sci.2019.53890.3473ENF. NasirpourSchool of electrical and computer engineering, University of Tehran, North Kargar Ave.,Tehran 14395/515, IranM.H. SamimiSchool of electrical and computer engineering, University of Tehran, North Kargar Ave.,Tehran 14395/515, Iran0000-0001-7536-9484H. MohseniSchool of electrical and computer engineering, University of Tehran, North Kargar Ave.,Tehran 14395/515, IranJournal Article20190627Power transformers have vital importance in the power delivery and, therefore, different diagnostic techniques are proposed for them. The frequency response analysis (FRA) is an effective method which detects the mechanical changes in transformer windings by extracting the transfer function. There are various approaches for obtaining the transfer function online, which is known as the online FRA technique. This paper compares these different mathematical approaches for obtaining the transfer function of a transformer. The comparison is carried out by defining an appropriate model for the transformer and applying these mathematical methods to it. The effect of other power network equipment on the transformer transfer function is also studied in this paper. The results of this contribution determine the proper methods for the online FRA technique, which can be used in the transformer monitoring applications.https://scientiairanica.sharif.edu/article_21549_db1d9dc7d514c796964ad793b20b32cb.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201A response-based approach to online prediction of generating unit angular stability359236052159510.24200/sci.2019.53966.3517ENA.A. HajnorouziCentre of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran.H.A. ShayanfarCentre of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran.Journal Article20190714In this paper, first, a rotor angle trajectory model based on polynomial functions is proposed. Afterwards, a response-based approach for online prediction of power system angular instability is presented. The proposed method utilizes bus phase angle data measured by phasor measurement unit at the point of common coupling of power plant transformer to the bulk power grid. In the prediction process, by computing the second order derivative of post-fault data, the starting point of the calculation data window is determined. Next, a fifth-degree polynomial curve is fitted on the designated data window to predict the angular curve of generating unit. Based on the sign of the first order derivative of predicted curve, the angular stability of generating unit is judged. This approach is testified on the western system coordinating council standard test bed under different operation and fault type scenarios. Taking into account various fault conditions and their associated occurrence probability, a probabilistic index is also defined to sum up the overall performance of the new method. Simulation results confirm that the proposed method outperforms the existing ones in terms of both accuracy and speed. Prediction results could be used in generator rejection schemes to prevent severe power plant outages.https://scientiairanica.sharif.edu/article_21595_c311ddeed14c93c896849b77be0394a5.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Decentralized energy trading framework for active distribution networks with multiple microgrids under uncertainty360636212155810.24200/sci.2019.53962.3557ENM. DoostizadehFaculty of Engineering, Lorestan University, 5 km Tehran Road, Khorramabad, P.O. Box 68151-44316, Lorestan, Iran.0000-0002-7045-6439M.R. ShakaramiFaculty of Engineering, Lorestan University, 5 km Tehran Road, Khorramabad, P.O. Box 68151-44316, Lorestan, Iran.H. BastamiFaculty of Engineering, Lorestan University, 5 km Tehran Road, Khorramabad, P.O. Box 68151-44316, Lorestan, Iran.Journal Article20190717The ever-increasing need for more reliable power supply, cost-effective and environmental-friendly utilization of distributed energy resources will result in formation of multiple microgrids (MMGs) in the near future of distribution system. To achieve this prospective, a coordination among MMGs is necessary. Accordingly, this paper proposes a new non-hierarchical multilevel architecture for the optimal scheduling of active distribution network (ADN) with MMGs. The proposed model is a decentralized decision making algorithm to optimally coordinate the mutual interaction between local optimization problems of ADN and MMGs. A non-hierarchical analytical target cascading (ATC) method is presented to solve the local optimization problems in parallel. Also, underlying risks of the energy trading caused by renewable generation uncertainty are reflected in both the objective functions and the constraints of local optimization problem. The numerical results on modified IEEE 33-bus distribution test system containing two microgrids demonstrate the effectiveness and merits of proposed model.https://scientiairanica.sharif.edu/article_21558_aa0395a7bbcb02611a479934297da60a.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Micro-grids bidding Strategy in a Transactive energy market362236342156210.24200/sci.2019.54148.3616ENH. NezamabadiDepartment of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, IranV. VahidinasabDepartment of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran0000-0002-0779-8727Journal Article20190731This paper proposes microgrid (MG) bidding strategy in the transactive energy market (TEM), in which market participants are able to negotiate and trade by a new-designed smart contract (SC) in a peer-to-peer way. In such a market, MG can balance its deviations, which are the resultant of the intermittency of the renewable energy sources, and the volatility of the load. In this paper, the uncertainty is handled by interval optimization. By participation in the TEM, the MG bidding problem is a bi-level optimization with interval coefficient, in which the MG’s profit maximizes in the upper level and the rivals’ behaviour in the TEM are modelled in the lower level. In order to solve the aforementioned problem, the proposed model recasts as a single-level interval optimization problem by the Karush-Kuhn-Tucker (KKT) conditions and the interval optimization concept. Simulation results show the applicability of the proposed model and realize the 1.7% increase in the MG profit for a 4-hour duration basis TEM.https://scientiairanica.sharif.edu/article_21562_d319e64b08d1ef4c7c0eba780b6f1540.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Reliability enhancement of active distribution grids via emergency V2G programs: An analytical cost/worth evaluation framework363536452156310.24200/sci.2019.54158.3624ENH. FarzinDepartment of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, P.O. Box 61357-43337,
Iran.M. MonadiDepartment of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, P.O. Box 61357-43337,
Iran.Journal Article20190802In this paper, an analytical cost/worth evaluation framework is presented for determination of optimal reliability enhancement plan in active distribution systems. In this regard, it is assumed that emergency vehicle-to-grid (V2G) programs provided by parking lots will be deployed for supplying loads during network outages, and optimal number of participating electric vehicles (EVs) is determined using a reliability cost/worth method. According to the presented framework, distribution system annual social costs are minimized. Annual social costs include annualized investment costs, annual operating costs, annual revenues as well as annual customer interruption costs (CIC). Therefore, estimation procedure of different cost terms are introduced. Moreover, an appropriate analytical framework is presented for calculation of CIC. Furthermore, a simple method is presented for estimating the financial costs of emergency V2G programs. The presented framework is implemented on a test system, various emergency V2G programs are investigated, and the results are discussed.https://scientiairanica.sharif.edu/article_21563_120e4ee4e86f0f22b3a2f54b161224a9.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Incorporating bus-bar switching actions into AC optimal power flow to ovoid over-current status364636552161110.24200/sci.2019.54166.3625ENM.A. TavakkoliDepartment of Electrical and Computer Engineering, Semnan University, Semnan, P.O. Box 35195-363, IranN. AmjadyDepartment of Electrical and Computer Engineering, Semnan University, Semnan, P.O. Box 35195-363, Iran.0000-0003-1308-1738Journal Article20190803This paper presents a new AC optimal power flow (AC OPF) model for sub-transmission networks. This model, which consists of sub-transmission and distribution bus-bar switching actions, can avoid undesirable over-current (OC) status and subsequent actions of OC relays. The proposed AC OPF optimizes the bus-bar switching actions along with optimizing sub-transmission control actions. Also, to consider the impact of OC relays’ actions in the proposed AC OPF, the cost of load shedding caused by these relay actions is included in the objective function and is minimized along with the sub-transmission operation cost. The bus-bar switching actions are modeled using binary decision variables. Therefore, the proposed AC OPF model is formulated as a Mixed Integer Non-linear Programming (MINLP) optimization problem. The effectiveness of the proposed model is illustrated on a real-world sub-transmission network of Iran’s power system.https://scientiairanica.sharif.edu/article_21611_00abe04d0c3e92ae16e873a83f1c230d.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201A new computing perturb-and-observe-type algorithm for MPPT in solar photovoltaic systems and evaluation of its performance against other variants by experimental validation365636712155010.24200/sci.2019.54183.3635ENV. BhanDepartment of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, PakistanA.A. HashmaniDepartment of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.M.M. Shaikhb. Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology, Jamshoro, Pakistan.;
Supply Chain and Operations Management Research Group, Mehran University of Engineering and Technology, Jamshoro,
Pakistan.0000-0002-1471-822XJournal Article20190805Solar energy is becoming a mainstream energy source with considerable attention from analysts these days. The photovoltaic (PV) system’s output power fluctuates with temperature and sunlight affecting its efficiency. To extract accessible power by PV system, maximum power point tracking (MPPT) method is used. A famous strategy, regularly utilized for simplicity and low cost, is the Perturb and Observe (PO) algorithm. However, there are a few downsides of PO algorithm, which result in power loss and low efficiency. We evaluate the performance of the conventional PO against some of its enhancements, specially a recent PO-variant, for MPPT. Experiments are conducted at different irradiances and temperature levels in two ways: with load and with battery, by conventional PO and its variants. Outlining strategy to reach optima and stability of the methods are discussed. The PO variants are rated from view-points of stability, accuracy, post-MPP oscillations and tracking speed. The recommendations can prove to be fruitful for the practitioners working with MPPT in PV solar systems using PO algorithms. The validation of simulation results has been made using the real time experimental results. The new PO-variant appears to be a reliable computing algorithm for MPPT in solar PV systems.https://scientiairanica.sharif.edu/article_21550_b891bbecf7cd4a194b04f3cb50250569.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Distribution power system outage diagnosis based on root cause analysis367236802159610.24200/sci.2019.54190.3638ENM.S. BashkariSchool of Electrical and Computer Engineering, Department of Engineering, Shiraz University, Zand Avenue, Shiraz, P.O. Box
71348-51154, Iran.A. SamiSchool of Electrical and Computer Engineering, Department of Engineering, Shiraz University, Zand Avenue, Shiraz, P.O. Box
71348-51154, Iran.M. RastegarSchool of Electrical and Computer Engineering, Department of Engineering, Shiraz University, Zand Avenue, Shiraz, P.O. Box
71348-51154, Iran.M.J. BordbariSchool of Electrical and Computer Engineering, Department of Engineering, Shiraz University, Zand Avenue, Shiraz, P.O. Box
71348-51154, Iran.Journal Article20190807This paper proposes data mining-based models to diagnose outage data in distribution power systems. In this work, outage data from a local distribution company is gathered and aligned with weather data. Then, a subset of features is selected to reduce the processing time and simplifying purposes. To increase the fairness of final models and to account for differences in misclassification cost, using a customized cost matrix is proposed. Two decision tree-based modeling algorithms are trained and tested. Results show the ability of the established models to diagnose the root cause of an outage fairly well. In addition, an ensemble of the decision tree-based models is built, which outperforms the other two models in almost all cases. Finally, applications of such models in decreasing outage duration and improving the reliability of the power distribution network are discussed.https://scientiairanica.sharif.edu/article_21596_585eb73379d9bb1afd59a2f9a5bb4bcb.pdfSharif University of TechnologyScientia Iranica1026-309826Special Issue on machine learning, data analytics, and advanced optimization techniques...20191201Enabling demand response potentials for resilient microgrid design368136932159710.24200/sci.2019.54235.3657ENM. ChegenizadehDepartment of Electrical Engineering, Sharif University of Technology, Tehran, Iran.A. SafdarianDepartment of Electrical Engineering, Sharif University of Technology, Tehran, Iran.Journal Article20190813The future microgrids (MGs) hosting a great deal of uncertain and intermittent local renewable generations are envisioned to need for fast and flexible units in the generation side. Demand response, however, as a load shaping tool can alleviate the needs. This paper proposes a model to consider demand response potentials activated by time-varying prices in MG design studies. The model aims at maximizing MG owner’s profit while technical limits and constraints are adhered. The model also ensures that the designed MG is resilient against islanding events. To handle complexity of the model, Benders decomposition is used to decompose the model into a master problem and two types of sub-problems. The master problem optimizes binary variables indicating installing status of generating units and batteries. The first type of sub-problems optimizes continuous variables, and the second ensures the resilient operation of the MG against islanding events. In the model, the uncertainties associated with load and intermittent generation resources are captured via a scenario-based stochastic approach. The demand behavior in response to time-varying prices is modeled via price elasticity coefficients. The effectiveness of the proposed model is demonstrated through extensive numerical studies and sensitivity analyses.https://scientiairanica.sharif.edu/article_21597_9b6867f9e583854346625b68969c9598.pdf