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Fine-grained image classification based on knowledge distillation

- Liu, Ying, Feng, Hao, Zhang, Weidong, Fang, Jie, Xiao, Peng, Zhang, Dengsheng

  • Authors: Liu, Ying , Feng, Hao , Zhang, Weidong , Fang, Jie , Xiao, Peng , Zhang, Dengsheng
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
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  • Description: Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model's generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security. © 2023 IEEE.

Identification of fake news : a semantic driven technique for transfer domain

- Ferdush, Jannatul, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal, Das, Raj

Improved model predictive torque control with reduced active prediction vectors for voltage source inverter driven induction motor drives

- Nahin, Nahin, Biswas, Shuvra, Hosain, Md Kamal, Bin Islam, Md Anas, Islam, Md Rabiul, Shah, Rakibuzzaman

Improved switching scheme to reduce the junction temperature and power loss of CHB inverters

- Afrin, Sadia, Biswas, Suvra, Bin Islam, Md Sabbir, Islam, Md Rabiul, Shah, Rakibuzzaman

Improved voltage balancing discontinuous PWM scheme for solar PV Fed grid-tied NPC inverters

- Hossain, Shahriar, Biswas, Shuvra, Islam, Md Rabiul, Shah, Rakibuzzaman

Machine learning driven digital twin for industrial control black box system : a novel framework and case study

- Siddiqui, Mustafa, Kahandawa, Gayan, Hewawasam, Hasitha, Rehman Siddiqi, Muftooh

Missing health data pattern matching technique for continuous remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew

Modeling and analysis of finite-scale clustered backscatter communication networks

- Wang, Qiu, Zhou, Yong, Dai, Hong-Ning, Zhang, Guopeng, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Qiu , Zhou, Yong , Dai, Hong-Ning , Zhang, Guopeng , Imran, Muhammad , Nasser, Nidal
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023, Rome, 28 May-1 June 2023, ICC 2023 - IEEE International Conference on Communications Vol. 2023-May, p. 1456-1461
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  • Description: Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets. © 2023 IEEE.

Novel few-shot learning based fuzzy feature detection algorithms

- Luo, Yun, Lu, Liangfu, Cui, Xudong, Du, Yan, Bi, Yingying, Zhu, Limin, Liang, Christy

  • Authors: Luo, Yun , Lu, Liangfu , Cui, Xudong , Du, Yan , Bi, Yingying , Zhu, Limin , Liang, Christy
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023, Thessaloniki, Greece, 9-12 October 2023, 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
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  • Description: The Internet of Things (IoT) has significantly enhanced various aspects of our daily lives, including security, health, education, and energy efficiency, among others. Within the realm of IoT, image classification stands as a pivotal technique that has achieved notable success in domains such as facial recognition within security and scene recognition in transportation for traffic analysis. Nonetheless, the challenge emerges when tackling classification tasks with only limited labeled samples available for each category. Conventional machine learning techniques often struggle to attain satisfactory classification results under such circumstances. To address this issue, the concept of few-shot learning has emerged, aiming to achieve effective classification using only a small number of labeled samples. State-of-the-art few-shot learning models have introduced novel frameworks to tackle this problem. However, the inherent ambiguity and uncertainty within data often hinder the performance of classification methods. To overcome this limitation, this paper proposes the integration of fuzzy learning with few-shot learning in the context of feature extraction. The objective is to mitigate data fuzziness and enhance model performance. Leveraging a fuzzy extraction algorithm, we introduce fuzzy prototype networks and a fuzzy graph neural network with fuzzy reasoning. These frameworks are designed to analyze noisy and uncertain data, utilizing convolutional neural networks for feature extraction and applying fuzzy reasoning to capture ambiguity representations for features within each fuzzy set. The SoftMax function is then normalized to serve as a feature weight, effectively constraining the original feature vector. The effectiveness and efficiency of our proposed model are demonstrated through experimental evaluations conducted on various public datasets. The results showcase the model's capability in addressing the challenges posed by limited labeled data and data uncertainty, thus reaffirming its potential in enhancing the performance of image classification tasks within the IoT context. © 2023 IEEE.

Radiative heat transfer in solar particle receivers

- Chen, Jingling, Kumar, Apurv, Coventry, Joe, Lipiński, Wojciech

Scalar reward is not enough JAAMAS Track

- Vamplew, Peter, Smith, Benjamin, Källström, Johan, Ramos, Gabriel, Rădulescu, Roxana, Roijers, Diederik, Hayes, Conor, Heintz, Frederik, Mannion, Patrick, Libin, Pieter, Dazeley, Richard, Foale, Cameron

Secure-and privacy-preserving policies for distributed cooperative control of multiple vehicle systems

- Nguyen, Binh, Nghiem, Truong, Nguyen, Linh, Nguyen, Tuy, Nguyen, Thang

  • Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Tuy , Nguyen, Thang
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, Orlando USA, 2-4 May 2023, Proceedings of SPIE - The International Society for Optical Engineering Vol. 12540
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  • Description: Conventional distributed formation control of multiple vehicle systems (MVSs) has two drawbacks: inflexible formation changes and explicit-value exchanges of vehicles' information such as position and velocity among all vehicles. Since formation changes are needed, each vehicle is required to update its relative position which is quite difficult in large spatial applications. Firstly, the explicit-value exchanges possibly result in two critical issues. When each vehicle policy needs to keep its information confidential from another unexpected listener, the explicit-value exchanges are invalid for the privacy policies. Additionally, the explicit-value storing or exchanging signals or parameters are much more vulnerable and dangerous to security threats. This work proposes an approach to overcome the above challenges by taking advantage of model predictive control-consensus algorithms to achieve desired formations. We will also allow the computation to be effectively distributed among the vehicle agents according to their computational capabilities. Secondly, we use the highly secure encryption scheme that empowers all computations carried out in encrypted forms, including system parameters and signals. Our results are verified by the formation control of multiple vehicles working in large-scale environments where a ground station does not touch all vehicles due to limited communication ranges and security problems. Compared to cutting-edge studies, the formation of vehicles is still able to be changed securely by the ground station without updating new formations to all vehicles. Besides, the data privacy of each vehicle is preserved by encrypting all physical signals. © 2023 SPIE. All rights reserved.

SMGKM : an efficient incremental algorithm for clustering document collections

- Bagirov, Adil, Seifollahi, Sattar, Piccardi, Massimo, Zare Borzeshi, Ehsan, Kruger, Bernie

Time-minimum motion handling of open liquid-filled objects using sparse sequential quadratic programming

- Le, Hieu, Appuhamillage, Gayan, Nguyen, Linh

  • Authors: Le, Hieu , Appuhamillage, Gayan , Nguyen, Linh
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, Sivas, Turkey, 11-13 October 2023, 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023
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  • Description: The paper presents an efficient approach to minimize motion time of an industrial robot so that it can successfully manipulate an open and liquid-filled object in pick-and-place operations. It is first proposed a motion planning optimization problem, where the total motion duration is considered as a cost function. Moreover, the robot physical limits such as its joint positions, velocities and accelerations are used as the optimization constrains. On the other hand, to ensure an open and liquidfilled object always upright, orientation constraints of the robot end-effector are taken into account. More specifically, roll and pitch of the end-effector are proposed to be fixed during the transportation, which ensures there is no tipping over in the object. The formulated motion planning optimization problem is then efficiently solved by using the sparse sequential quadratic programming method. Our approach excels in optimizing the motion trajectory by leveraging its flexibility, accommodating various trajectory shapes that satisfy the kinematic conditions. The optimization leads to more efficient and effective motion execution, resulting in a substantial reduction in the overall motion profile duration. Extensive evaluation of the proposed approach on a KUKA robot model demonstrates its effectiveness. © 2023 IEEE.

Whose data are reliable : sensor declared data reliability

- Shafin, Sakib, Karmakar, Gour, Mareels, Iven, Balasubramanian, Venki, Kolluri, Ramachandra

  • Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven , Balasubramanian, Venki , Kolluri, Ramachandra
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, Montreal, Canada, 21-23 June 2023, International Conference on Wireless and Mobile Computing, Networking and Communications Vol. 2023-June, p. 249-254
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  • Description: Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor's historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications. © 2023 IEEE.

A cloud-based IoMT data sharing scheme with conditional anonymous source authentication

- Wang, Yan-Ping, Wang, Xiao-Fen, Dai, Hong-Ning, Zhang, Xiao-Song, Su, Yu, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Yan-Ping , Wang, Xiao-Fen , Dai, Hong-Ning , Zhang, Xiao-Song , Su, Yu , Imran, Muhammad , Nasser, Nidal
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 2915-2920
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  • Description: As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical. © 2022 IEEE.

A generative adversarial active learning method for effective outlier detection

- Bah, Mohamed, Zhang, Ji, Yu, Ting, Xia, Feng, Li, Zhao, Zhou, Shuigeng, Wang, Hongzhi

  • Authors: Bah, Mohamed , Zhang, Ji , Yu, Ting , Xia, Feng , Li, Zhao , Zhou, Shuigeng , Wang, Hongzhi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022, Virtual, online, 31 October-2 November 2022, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI Vol. 2022-October, p. 131-139
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  • Description: Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem. © 2022 IEEE.

A new modulation technique for H6 transformerless inverter to minimize leakage current with reduced power loss

- Mondal, Sudipto, Biswas, Shuvra, Islam, Md Rabiul, Shah, Rakibuzzaman

  • Authors: Mondal, Sudipto , Biswas, Shuvra , Islam, Md Rabiul , Shah, Rakibuzzaman
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022, Kharagpur, India, 9-11 December 2022, 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
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  • Description: Solar energy has a significant role to play in supplying the world's growing energy needs, while transformerless inverters (TLIs) are effectively used in photovoltaic (PV) applications due to their high efficiency, small size, and lower cost. Though transformer-based inverter is frequently employed to offer galvanic isolation and voltage ratio conversions between input and output, they're getting less popular due to high iron and copper loss. Among various types of single-phase transformerless inverters, most of their switching techniques are based on the unipolar pulse width modulation (UPPWM) or bipolar pulse width modulation (BPPWM) method. Due to some unbalanced gate pulses in both UPPWM and BPPWM techniques, total harmonic distortion (THD) of output voltage (without filter) increases significantly. The leakage current also increases due to high harmonics content in the output power of TLI systems. The asymmetrical switching of the power semiconductor switches leads to a substantial increase in power loss due to unbalanced gate pulses. In this paper, a new modulation technique is proposed to reduce the output voltage THD, leakage current, as well as the total power losses compared to mostly used UPPWM and BPPWM techniques employing in PV fed TLI system. The mostly used H6 transformerless inverter topology is chosen here for performance evaluation. The simulation is carried out in MATLAB/Simulink and PLECS simulation environments to validate the proposed claim. By using the H6 transformerless inverter topology, 12.2 mA of common mode current is achieved by maintaining a steady common mode voltage via the proposed modulation technique. The output voltage (without filter) THD is found to be 37.5% with the proposed modulation technique, while BPPWM and UPPWM show 98.7% and 53.8% THD, respectively. A reduced-scaled laboratory prototype is built and tested to verify the simulated claim. © 2022 IEEE.

A new transformer-less common grounded nine-level grid-connected boost inverter

- Kurdkandi, Naser, Marangalu, Milad, Haghighian, Saiedeh, Islam, Md Rabiul, Mehrizi-Sani, Ali, Shah, Rakibuzzaman

  • Authors: Kurdkandi, Naser , Marangalu, Milad , Haghighian, Saiedeh , Islam, Md Rabiul , Mehrizi-Sani, Ali , Shah, Rakibuzzaman
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022, Kharagpur, India, 9-11 December 2022, 1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
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  • Description: In this paper, a new single-source switched-capacitor-based 9-level structure that is applied to grid systems is presented. In this presented circuit, the null of the grid and input source has the common ground point. As a result, the leakage current is suppressed completely. The introduced solution can provide a nine-level output voltage waveform and boost the input voltage amplitude using a single input source. The high step-up factor of the introduced inverter is 2. In this topology, the switched capacitors are fixed to input the dc power supply and provide the voltage boosting feature. The mathematical analysis of the output filter is expressed. A peack current control method is applied to generate the gate pulses and control both active and reactive powers. A thorough comparison has been made to highlight the proposed structure's features and show the difference between the proposed structure and other structures. Finally, to confirm the correct operation of the proposed structure, its electrical circuit was simulated using MATLAB/Simulink software, and the results were extracted considering different operational conditions. © 2022 IEEE.

A review of cascade water supply systems

- Pathberiyage, Githmi, Barton, Andrew, Kandra, Harpreet, Dassanayake, Kithsiri

  • Authors: Pathberiyage, Githmi , Barton, Andrew , Kandra, Harpreet , Dassanayake, Kithsiri
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 40th Hydrology and Water Resources Symposium, HWRS 2022, Brisbane, Australia, 30 November to 2 December 2022, Hydrology and Water Resources Symposium, HWRS 2022 p. 679-694
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  • Description: Cascade Water Supply Systems (CWSS) are a type of rural water supply system used in many parts of the world such as India, China, Sri Lanka, South America, Iran, Iraq, Saudi Arabi, Korea, Peru, Egypt, Rome, Turkey, Greece, and Thailand. They are also known as Village Tank Cascade Systems (VTCS) & Cascade Systems (CS). CWSS is typically designed to collect runoff from upper forested catchment areas to provide to downstream areas and consists of a network of linearly inter-connected 'tanks' or storages, with the supply of water often supplemented from additional catchments downstream, along with groundwater resources and diversions from other sources such as rivers. As water flows from the upper regions to the downstream segments of the system, water is utilised for various purposes such as irrigation, drinking, bathing, and other household activities. The inflows and outflows result in changes in water quality in different stretches of these systems. This paper reviews the international literature surrounding CWSS, with a particular focus on water quality and associated issues. It has been found that despite the significant social, environmental, and economic importance of CWSS, and their existence for many centuries, there is limited information on water quality characteristics over space and time. Pressures such as population growth, intensification of agricultural practices, and changing climate, affect these systems as well. This review reveals that water quality is comparatively better in the upstream sections of CWSSs and progressively worsens downstream, with the data showing that the water quality in downstream systems clearly exceeds the WHO drinking and irrigation water standards. © Hydrology and Water Resources Symposium, HWRS 2022. All rights reserved.