Khan, Muhammad Umer

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/6656
Name Variants
Khan, Muhammad Umer
K.,Muhammad Umer
Muhammad Umer, Khan
Khan,Muhammad Umer
M.U.Khan
M., Khan
M.,Khan
Khan U.
Khan M.
Khan,M.U.
M. U. Khan
Umer Khan M.
K., Muhammad Umer
Muhammad Umer Khan
Khan, Umer
Khan, Muhammed Umer
Khan, M. U.
Khan, M.U
Job Title
Yardımcı Doçent
Email Address
umer.khan@atilim.edu.tr
Main Affiliation
Mechatronics Engineering
Status
Website
ORCID ID
0000-0002-9195-3477
Scopus Author ID
57209876827
Turkish CoHE Profile ID
277006
Google Scholar ID
8h-JrH8AAAAJ
WoS Researcher ID
N-5478-2016

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
4
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
4
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
1
Research Products
Documents

38

Citations

641

h-index

13

Go to Scopus profile
Documents

30

Citations

476

Go to WoS profile
Scholarly Output

38

Articles

15

Views / Downloads

185/1662

Supervised MSc Theses

11

Supervised PhD Theses

0

WoS Citation Count

253

Scopus Citation Count

374

Patents

0

Projects

0

WoS Citations per Publication

6.66

Scopus Citations per Publication

9.84

Open Access Source

10

Supervised Theses

11

JournalCount
Applied Sciences2
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018 -- 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018 -- 2 July 2018 through 4 July 2018 -- Oulu -- 1391112
2019 2nd International Conference on Communication, Computing and Digital Systems, C-CODE 2019 -- 2nd International Conference on Communication, Computing and Digital Systems, C-CODE 2019 -- 6 March 2019 through 7 March 2019 -- Islamabad -- 1469971
2020 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 -- 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 -- 14 April 2020 through 16 April 2020 -- Antalya -- 1604501
2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021 -- 2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021 -- 21 April 2021 through 23 April 2021 -- Virtual, Online -- 1767941
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Author: Alam, M.S.

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 3
    Investigation of Harmonic Losses To Reduce Rotor Copper Loss in Induction Motors for Traction Applications
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Siddique, M.S.; Ertan, H.B.; Alam, M.S.; Khan, M.U.
    The focus of this paper is to seek means of increasing induction motor efficiency to a comparable level to a permanent magnet motor. Harmonic and high-frequency losses increase the rotor core and copper loss, often limiting IM efficiency. The research in this study focuses on reducing rotor core and copper losses for this purpose. An accurate finite element model of a prototype motor is developed. The accuracy of this model in predicting the performance and losses of the prototype motor is verified with experiments over a 32 Hz–125 Hz supply frequency range. The verified model of the motor is used to identify the causes of the rotor core and copper losses of the motor. It is found that the air gap flux density of the motor contains many harmonics, and the slot harmonics are dominant. The distribution of the core loss and the copper loss is investigated on the rotor side. It is discovered that up to 35% of the rotor copper losses and 90% rotor core losses occur in the regions up to 4 mm from the airgap where the harmonics penetrate. To reduce these losses, one solution is to reduce the magnitude of the air gap flux density harmonics. For this purpose, placing a sleeve to cover the slot openings is investigated. The FEA indicates that this measure reduces the harmonic magnitudes and reduces the core and bar losses. However, its effect on efficiency is observed to be limited. This is attributed to the penetration depth of flux density harmonics inside the rotor conductors. To remedy this problem, several FEA-based modifications to the rotor slot shape are investigated to place rotor bars deeper than the harmonic penetration. It is found that placing the bars further away from the rotor surface is very effective. Using a 1 mm sleeve across the stator’s open slots combined with a rotor tapered slot lip positions the bars slightly deeper than the major harmonic penetration depth, making it the optimal solution. This reduces the bar loss by 70% and increases the motor efficiency by 1%. Similar loss reduction is observed over the tested supply frequency range. © 2025 by the authors.
  • Thumbnail Image
    Conference Object
    Reinforcement Learning-Based Multi-Robot Path Planning and Congestion Management in Warehouse Order Picking
    (Institute of Electrical and Electronics Engineers Inc., 2024) Alam, M.S.; Khan, M.U.; Gunes, A.
    This paper addresses the multi-robot path planning problem in a warehouse environment using reinforcement learning. The warehouse layout comprises of a grid map with multiple robots for retrieval and delivery of orders, inventory pods for storage, and pick stations for receiving outbound orders. The robots are required to pick and deliver orders from target shelves to their corresponding pick stations by navigating in a complex network of aisles. Q-learning algorithm computes optimal paths for the robots, while avoiding congestion in the aisles. Simulation results demonstrate the efficacy of the proposed method in optimizing both travel time and travel distance, thus enhancing the overall operational efficiency of the warehouse. © 2024 IEEE.