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Cost effective annotation framework using zero-shot text classification

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren

  • Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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  • Description: Manual and high-quality annotation of social media data has enabled companies and researchers to develop improved implementations using natural language processing. However, human text-annotation is expensive and time-consuming. Crowd-sourcing platforms such as Amazon's Mechanical Turk (MTurk) can be leveraged for the creation of large training corpora for text classification tasks using social media data. Nevertheless, the quality of annotations can vary significantly, based on the interpretations and motivations of annotators completing the tasks. Further, the labelling cost of data through MTurk will increase if target messages are small and having a significant amount of noise (e.g. promotional messages on Twitter). In this work, we propose a new annotation framework to create high-quality human-annotated datasets for text classification from social media data. We present a zero-shot text classification based pre-annotation technique reducing the adverse effects arising due to the highly skewed distribution of data across target classes. The proposed framework significantly reduces the cost and time while maintaining the quality of the annotations. Being generic, it can be applied to annotating text data from any discipline. Our experiment with a Twitter data annotation using the proposed annotation framework shows a cost reduction of 80% with no compromise to quality. © 2021 IEEE.

Cross network representation matching with outliers

- Hou, Mingliang, Ren, Jing, Febrinanto, Febrinanto, Shehzad, Ahsan, Xia, Feng

  • Authors: Hou, Mingliang , Ren, Jing , Febrinanto, Febrinanto , Shehzad, Ahsan , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, online, 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 951-958
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  • Description: Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network. © 2021 IEEE.

Cybersecurity risks in meat processing plant and impacts on total productive maintenance

- Chundhoo, Vickram, Chattopadhyay, Gopinath, Karmakar, Gour, Appuhamillage, Gayan

  • Authors: Chundhoo, Vickram , Chattopadhyay, Gopinath , Karmakar, Gour , Appuhamillage, Gayan
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management; ICMIAM 2021, Ballarat; 12-15 December 2021
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  • Description: Technological changes have been happening in production facilities including food manufacturing industries in an ever-increasing rate. This includes advancement in data capture devices, signal processing, communication capabilities and automated process control systems such as Internet of Things. It is more challenging where production systems are highly reliant on automation and robotics. Remote performance monitoring and controls are becoming progressively vulnerable due to risks associated with cyber security and corporate espionage. May 2021 cyber-Attack forced JBS meats USA to pay 11m in ransom money to stop any further disruptions in services. This heavily impacted JBS global operations including JBS Australian food manufacturing facilities. Food production facilities in Australia have critical control points supported by smart technologies as part of their food safety management systems. Cyber-Attacks on production facilities could result in financial, operational, health and safety consequences. As survey by the Australian Cyber Security Centre in 2020 revealed that Australian small businesses are impacted by cybercime each year with a loss of 300m. To present the potential cyber security threats and their associated risk level, a case study is presented based on the processing and manufacture of meat products in Australia. From this case study, to protect the meat industries from attacks, we identify cyber security attacks and their possible mitigation strategies. This research shows cyber security attacks can severely affect Overall Equipment Effectiveness which motivate us to embed cyber security as an additional pillar in existing 8 pillars Total Productive Maintenance. If cyber security is added as additional pillar, it will improve the quality of end products and overall productivity of manufacturing industries. © 2021 IEEE.

D-optimal design for information driven identification of static nonlinear elements

- Ulapane, Nalika, Thiyagarajan, Karthick, Kodagoda, Sarath, Nguyen, Linh

  • Authors: Ulapane, Nalika , Thiyagarajan, Karthick , Kodagoda, Sarath , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 492-497
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  • Description: Identification of static nonlinear elements (i.e., nonlinear elements whose outputs depend only on the present value of inputs) is crucial for the success of system identification tasks. Identification of static nonlinear elements though can pose several challenges. Two of the main challenges are: (1) mathematical models describing the elements being unknown and thus requiring black-box identification; and (2) collection of sufficiently informative measurements. With the aim of addressing the two challenges, we propose in this paper a method of predetermining informative measurement points offline (i.e., prior to conducting experiments or seeing any measured data), and using those measurements for online model calibration. Since we deal with an unknown model structure scenario, a high order polynomial model is assumed. Over fit and under fit avoidance are achieved via checking model convergence via an iterative means. Model dependent information maximization is done via a D-optimal design of experiments strategy. Due to experiments being designed offline and being designed prior to conducting measurements, this method eases off the computation burden at the point of conducting measurements. The need for in-the-loop information maximization while conducting measurements is avoided. We conclude by comparing the proposed D-optimal design method with a method of in-the-loop information maximization and point out the pros and cons. The method is demonstrated for the single-input-single-output (SISO) static nonlinear element case. The method can be extended to MISO systems as well. © 2021 IEEE.

Decision behavior based private vehicle trajectory generation towards smart cities

- Chen, Qiao, Ma, Kai, Hou, Mingliang, Kong, Xiangjie, Xia, Feng

  • Authors: Chen, Qiao , Ma, Kai , Hou, Mingliang , Kong, Xiangjie , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Web Information Systems and Applications, WISA 2021 Vol. 12999 LNCS, p. 109-120
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  • Description: In contrast with the condition that the trajectory dataset of floating cars (taxis) can be easily obtained from the Internet, it is hard to get the trajectory data of social vehicles (private vehicles) because of personal privacy and government policies. This paper absorbs the idea of game theory, considers the influence of individuals in the group, and proposes a decision behavior based dataset generation (DBDG) model of vehicles to predict future inter-regional traffic. In addition, we adopt simulation tools and generative adversarial networks to train the trajectory prediction model so that the private vehicle trajectory dataset conforming to social rules (e.g., collisionless) is generated. Finally, we construct from macroscopic and microscopic perspectives to verify dataset generation methods proposed in this paper. The results show that the generated data not only has high accuracy and is valuable but can provide strong data support for the Internet of Vehicles and transportation research work. © 2021, Springer Nature Switzerland AG.

Decision-making in complex asset life extension

- Morey, Stephen, Chattopadhyay, Gopinath, Larkins, Jo-Ann

  • Authors: Morey, Stephen , Chattopadhyay, Gopinath , Larkins, Jo-Ann
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021; Ballarat; 12-15 December 2021
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  • Description: Complex, long-life assets present challenges for life cycle management, particularly decisions about life extension at the end of the asset life. Multiple interlinked risk factors drive the decision-making process. Compounding the difficulty, few ready-To-use life extension methods exist in literature. Complex decision analysis processes which are technically superior, but difficult to follow, may have the effect of alienating decision-makers who do not understand them. A method developed for NASA for risk informed decision making appears more suitable for guiding decision-making in life extension problems. This paper proposes a framework for decision-making in life extension of complex, long-life, capital-intensive assets, addressing some of the important challenges in life extension decision-making. © 2021 IEEE.

Deep video anomaly detection : opportunities and challenges

- Ren, Jing, Xia, Feng, Liu, Yemeng, Lee, Ivan

  • Authors: Ren, Jing , Xia, Feng , Liu, Yemeng , Lee, Ivan
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 959-966
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  • Description: Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection. © 2021 IEEE.

Delivery of online electronics and mechatronics labs during lockdowns

- Jayawardena, Amal, Kahandawa, Gayan, Piyathilaka, Lasitha

Detection of android malware using tree-based ensemble stacking model

- Shafin, Sakib, Ahmed, Md Maroof, Pranto, Mahmud, Chowdhury, Abdullahi

  • Authors: Shafin, Sakib , Ahmed, Md Maroof , Pranto, Mahmud , Chowdhury, Abdullahi
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
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  • Description: Increasing use of smartphones for everyday activities from banking, education to social networking is putting our personal information at risk as smartphone operating systems and applications are vulnerable to various types of attacks including malware attack. To this end Android operating system is particularly targeted as it is the most widely used mobile operating system. Building a robust detection system that can provide protection against recent attacks and can deliver not only accurate detection but also the type of the attack in order to protect the system is vital. In this study, we propose a twolayer Machine Learning detection model based on Ensemble Learning and Stacked Generalization method to accurately predict and classify the growing attacks on Android smartphones. We evaluated the proposed model on a very recent dataset, named CIC-Maldroid-2020, which contains 11,598 samples with various malicious attack types. The performance of our proposed model was evaluated on widely used metrics, like accuracy, precision, recall & F1-score. It outperforms previous studies done on the same dataset and achieves an accuracy of 99.49% in classifying each attack type. © IEEE 2022.

Detection of Malleefowl Mounds from Point Cloud Data

- Parvin, Nahida, Awrangjeb, Mohammad, Irvin, Marc, Florentine, Singarayer, Murshed, Manzur, Lu, Guojun

  • Authors: Parvin, Nahida , Awrangjeb, Mohammad , Irvin, Marc , Florentine, Singarayer , Murshed, Manzur , Lu, Guojun
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021, Gold Coast, 29 November to 1 December 2021
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  • Description: Airborne light detection and ranging (LiDAR) data have become cost and time-efficient means for estimating the size of timid fauna populations through the identification of artefacts that evidence their occurrence in a large, hostile geographic area. The unobtrusive detection method helps conservation managers to assess the stability of a population and to design appropriate conservation programs. Here we propose a mound (nest) detection method for Australia's native iconic bird, the Malleefowl, from point cloud data, which can be manipulated to act as a surrogate for population data. Existing detection methods are largely through manual observations, and are therefore not efficient for covering large and remote areas. The proposed mound detection method can identify mound feature based on height and intensity values provided by the point cloud data. Each candidate mound point is initially selected by applying a height threshold utilising the classified ground points and their corresponding digital elevation model (DEM). Then, another threshold based on intensity range derived from ground truth mound area analysis is applied on the extracted initial mound points to find the final candidate mound points. These extracted points are then used to generate a binary mask where the potential mound points are found sparse. To connect those points, a morphological filter is applied on the binary image and found the mound separated from other remaining non-mound objects. To obtain the mound from other non-mound objects, a morphological cleaning operation and a connected component analysis are carried out on the mask. The non-mound objects are removed from the mask utilising the area property of mound derived from the empirical analysis of ground-truth observations. Finally, the effectiveness of the proposed technique is calculated based on ground truth. Although the mound shapes and structures are highly variable in nature, our height and intensity-based mound point extraction method detected 55 % of the ground-truthed mounds. © 2021 IEEE.

Distributed denial of service attack detection using machine learning and class oversampling

- Shafin, Sakib, Prottoy, Shafin, Abbas, Saif, Hakim, Safayat, Chowdhury, Abdullahi, Rashid, Md Mamanur

  • Authors: Shafin, Sakib , Prottoy, Shafin , Abbas, Saif , Hakim, Safayat , Chowdhury, Abdullahi , Rashid, Md Mamanur
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: First International Conference on Applied Intelligence and Informatics, AII 2021, Nottingham, UK, July 30-31, 2021 Vol. 1435, p. 247-259
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  • Description: Distributed Denial of Services (DDoS) attack, one of the most dangerous types of cyber attack, has been reported to increase during the COVID-19 pandemic. Machine learning techniques have been proposed in the literature to build models to detect DDoS attacks. Existing works in literature tested their models with old datasets where DDoS attacks are not specific. These works mainly focus on detecting the presence of an attack rather than the type of DDoS attacks. However, detection of the attack type is vital for the review and analysis of enterprise-level security policy. Cyber-attacks are inherently an imbalanced data problem, but none of the models treated DDoS attack detection from this perspective. In this work, we present a machine learning model that takes the imbalance nature of the DDoS attack data into consideration for both presence/absence and the type of DDoS attack detection. Extensive experiment analysis with the recent and DDoS attack-specific dataset shows that the proposed technique can effectively identify DDoS attacks. © 2021, Springer Nature Switzerland AG.

Dynamic point cloud compression using a cuboid oriented discrete cosine based motion model

- Ahmmed, Ashek, Paul, Manoranjan, Murshed, Manzur, Taubman, David

  • Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 Vol. 2021-June, p. 1935-1939
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  • Description: Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs discrete cosine basis oriented motion models and the domains of such models are approximated by homogeneous regions called cuboids. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 4.17%. ©2021 IEEE

Dynamic point cloud geometry compression using cuboid based commonality modelling framework

- Ahmmed, Ashek, Paul, Manoranjan, Murshed, Manzur, Taubman, David

  • Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE International Conference on Image Processing, ICIP 2021, Anchorage, USA, 19-21 September 2021, Proceedings - International Conference on Image Processing, ICIP Vol. 2021-September, p. 2159-2163
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  • Description: Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected geometry and texture video frames are then encoded using modern video coding standard like HEVC. However, HEVC encoder is unable to exploit the global commonality that exists within a geometry frame and between successive geometry frames to a greater extent. This is because in HEVC, the current frame partitioning starts from a rigid 64 × 64 pixels level without considering the structure of the scene need be coded. In this paper, an improved commonality modeling framework is proposed, by leveraging on cuboid-based frame partitioning, to encode point cloud geometry frames. The associated frame-partitioning scheme is based on statistical properties of the current geometry frame and therefore yields a flexible block partitioning structure composed of cuboids. Additionally, the proposed commonality modeling approach is computationally efficient and has a compact representation. Experimental results show that if the V-PCC reference encoder is augmented by the proposed commonality modeling technique, a bit rate savings of 2.71% and 4.25% are achieved for full body and upper body of human point clouds’ geometry sequences respectively. © 2021 IEEE.

Dynamic VAr planning of large-scale PV enriched grid

- Alzahrani, Saeed, Mithulananthan, Nadarajah, Alshareef, Abdulrhman, Shah, Rakibuzzaman

  • Authors: Alzahrani, Saeed , Mithulananthan, Nadarajah , Alshareef, Abdulrhman , Shah, Rakibuzzaman
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2021, Brisbane, 5-8 December 2021
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  • Description: The integration of more inverter-based renewable resources (IBRs) would make the grid susceptible to large disturbances. Short term voltage instability is one of the key concerns for the renewable rich power system. An additional dynamic VAr support would be desirable to enhance system recovery. STATCOM is technically and financially promising solution which can provide dynamic Var support to the renewable rich power system. In this paper, system transient performance is assessed after synchronous generators (SGs) being significantly replaced by IBRs. To avoid the delayed recovery, STATCOM was integrated at the point of common coupling (PCC). Considering the changes in the grid's effective VAr, a framework was proposed to size the STATCOM. Moreover, the influence of distributed STATCOM on system performance was also examined. The proposed framework has been tested in the New England 39 bus system through simulation by DIgSILENT Power Factory. © 2021 IEEE

Dynamically regulated initialization for S-system modelling of genetic networks

- Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

  • Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, Virtual, Online, 13-15 October 2021, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021
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  • Description: Reverse engineering of gene regulatory networks through temporal gene expression data is an active area of research. Among the plethora of modelling techniques under investigation is the decoupled S-system model, which attempts to capture the non-linearity of biological systems in detail. For the model, number of parameters to be estimated are significantly high even when the network is of small or medium scale. Thus, the inference process poses a significant computational burden. In this paper, we propose: (1) a novel population initialization technique, Dynamically Regulated Prediction Initialization (DRPI), which utilises prior knowledge of biological gene expression data to create a feedback loop to produce dynamically regulated high-quality individuals for initial population; (2) an adaptive fitness function; and (3) a method for the maintenance of population diversity. The aim of this work is to reduce the computational complexity of the inference algorithm, to speed up the entire process of reverse engineering. The performance of the proposed algorithm was evaluated against a benchmark dataset and compared with other methods from earlier work. The experimental results show that we succeeded in achieving higher accuracy results in lesser fitness evaluations, considerably reducing the computational burden of the inference process. © 2021 IEEE.

E-Learning challenges for electronics and mechatronics education during lockdowns

- Jayawardena, Amal, Kahandawa, Gayan, Petty, Mark

E-Learning in Industrial Electronics during Covid-19

- Dunai, Larisa, Martins, Joao, Umetani, Kazuhiro, Lucia, Oscar, Ibrahim, Yousef, Appuhamillage, Gayan

  • Authors: Dunai, Larisa , Martins, Joao , Umetani, Kazuhiro , Lucia, Oscar , Ibrahim, Yousef , Appuhamillage, Gayan
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 22nd IEEE International Conference on Industrial Technology, ICIT 2021 Vol. 2021-March, p. 1227-1233
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  • Description: The educational methodologies employed in Industrial Electronics have been affected by Covid-19. In many cases, conventional learning methods relying on face-to-face lectures have been replaced by online methodologies. The whole process has required a fast adaptation and development of the e-learning tools to ensure a quality of theoretical, practical and laboratory lectures, as well as the development of new methods for the reliable assessment of the learning process. From this perspective, the present paper deals with the different strategies that have been implemented in institutions of several countries located in different geographical areas, including Portugal, Spain, Japan and Australia. It is shown that the use of methodologies, such as flip teaching, has provided a wide variety of possibilities to adapt to the new educational context. Moreover, for Industrial Electronics degrees, the use of virtual or remote laboratories, portable learning tools and advanced information and communication technologies have also risen as valuable resources. The paper also reports the problems arising during the development of the e-learning tools, their implementation constraints, and the evaluation of their results. © 2021 IEEE.

Ear in the sky : terrestrial mobile jamming to prevent aerial eavesdropping

- Wang, Qubeijian, Liu, Yalin, Dai, Hong-Ning, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Qubeijian , Liu, Yalin , Dai, Hong-Ning , Imran, Muhammad , Nasser, Nidal
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Global Communications Conference, GLOBECOM 2021, Madrid, 7-11 December 2021, 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
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  • Description: The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme. © 2021 IEEE.

Economic dispatch of model predictive controlled distributed power generation

- Shan, Yinghao, Hu, Jiefeng, Liu, Huashan

Effects of non-stationary forced oscillation on electromechanical modes

- Surinkaew, Tossaporn, Emami, Kianoush, Shah, Rakibuzzaman, Mithulananthan, Nadarajah

  • Authors: Surinkaew, Tossaporn , Emami, Kianoush , Shah, Rakibuzzaman , Mithulananthan, Nadarajah
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2021, Brisbane, 5-8 December 2021, 2021 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2021
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  • Description: The forced oscillation (FO) containing variable frequencies, known as non-stationary FO, has yet been well understood in power system engineers. This paper analyzes the effects of the non-stationary FO on electromechanical modes (EMs) in the range of 0.2 to 2.0 Hz. The non-stationary forced disturbance (FD) with such variable frequency bands is mathematically modeled. The injection of the non-stationary FD results in the non-stationary FO. A modified subspace-based state-space identification (so-called 4SID) method is applied to estimate the non-stationary frequencies and damping ratios of the EMs along with a moving window. Effects of the non-stationary FO on the EMs are compared to those of the conventional FO. Simulation results are verified in the future 14-machine Southeast Australian power system with converter controlled-based generations under various FO conditions. © 2021 IEEE