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"Perkembangan teknologi drone menyimpan beragam potensi bahaya bagi keamanan suatu wilayah. Dengan berkembangnya teknologi drone, sekelompok pihak dapat memantau secara ilegal atau bahkan melakukan penyerangan jarak jauh dengan mengendalikan drone menggunakan remote control maupun memprogram strategi pergerakan drone yang digunakan. Potensi bahaya teknologi drone menjadi semakin mengancam ketika drone yang digunakan untuk menyerang adalah bertipe drone berkelompok, yang memungkinkan terdapat sekelompok drone yang terkoordinasi secara sistematis untuk melakukan penyerangan terhadap suatu area.Meskipun saat ini telah berkembang beberapa teknologi penangkal drone, teknologi yang ada cenderung difokuskan untuk menangkal satu drone tunggal. Belum terdapat publikasi mendalam yang membahas kerangka kerja penangkal serangan drone berkelompok. Pada penelitian ini, dikembangkan suatu strategi pertahanan penangkal drone berkelompok (drone swarm) menggunakan pasukan autonomous-drone penjaga keamanan wilayah. Konsep usulan yang diajukan adalah dengan cara mengembangkan algoritma koordinasi yang memungkinkan suatu pasukan autonomous-drone penjaga keamanan wilayah untuk melakukan penangkapan atau penghentian secara terorganisir terhadap kelompok drone penyerang.Adapun dasar algoritma yang digunakan pada penelitian ini adalah Social Spider Optimization (SSO). Aspek utama kontribusi di dalam penelitian ini terdapat pada modifikasi algoritma SSO untuk mengatasi persoalan pencarian multi-target-bergerak berupa penangkapan atau penghentian serangan drone berkelompok. Penelitian ini telah diuji dalam simulator 3 dimensi, dengan konfigurasi pergerakan kelompok drone penyerang yang diujikan berbasis pendekatan penyerangan sekali jalan. Uji kinerja dilakukan dengan membandingkan kinerja usulan algoritma dengan 3 alternatif algoritma pergerakan kawanan drone pertahanan. Ketika dihadapkan dengan kelompok drone penyerang yang berkerumun, dibandingkan 3 alternatif algoritma pertahanan yang lain, usulan algoritma mampu mengurangi kerusakan hingga rasio 44% (terbaik), 67% (rata-rata), dan 97% (terburuk).

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Nowadays, drone technology development brings many dangerous possibilities for the security of an area. Because of the advanced development, many villains can conduct illegal surveillance or even long-distance attack on an area by remote controlling a drone or programming the drone movement strategy. Meanwhile, the dangerous impact of drone technology usage becomes higher when the technology used to attack an area is the type of drone swarm technology. In this kind of technology, every drone can coordinate and communicate while striking an area.

Although there have been many anti-drone technologies developed at the moment, they tend to focus on targeting one single drone only. So far, no publication comprehensively proposes a framework to overcome the multi-drone attack. This research presents an anti-drone-swarm defense strategy to protect area safety by using the autonomous-drone unit. The proposed method concept is manifested by developing a coordination algorithm for an autonomous-drone army to communicate with each other while catching or stopping the invader drone swarm.

The foundation of this research algorithm is Social Spider Optimization (SSO). This research's main contribution lies in modifying the SSO algorithm to handle the moving multiple-target searching problem, in this case, by catching or stopping the drone-swarm attack. This research has been tested in a 3D simulation environment where invader drone-swarm movement is developed based on a one-way-ticket approach. Performance evaluation is conducted by comparing the proposed method result with 3 other multiple defender drone movement algorithms. When facing an invader drone swarm with crowded formation, compared to the other 3 algorithms, the proposed method produces damage ratios up to 44% (best), 67% (average), and 97% (worst)."

"Perkembangan teknologi drone menyimpan beragam potensi bahaya bagi keamanan suatu wilayah. Dengan berkembangnya teknologi drone, sekelompok pihak dapat memantau secara ilegal atau bahkan melakukan penyerangan jarak jauh dengan mengendalikan drone menggunakan remote control maupun memprogram strategi pergerakan drone yang digunakan. Potensi bahaya teknologi drone menjadi semakin mengancam ketika drone yang digunakan untuk menyerang adalah bertipe drone berkelompok, yang memungkinkan terdapat sekelompok drone yang terkoordinasi secara sistematis untuk melakukan penyerangan terhadap suatu area.Meskipun saat ini telah berkembang beberapa teknologi penangkal drone, teknologi yang ada cenderung difokuskan untuk menangkal satu drone tunggal. Belum terdapat publikasi mendalam yang membahas kerangka kerja penangkal serangan drone berkelompok. Pada penelitian ini, dikembangkan suatu strategi pertahanan penangkal drone berkelompok (drone swarm) menggunakan pasukan autonomous-drone penjaga keamanan wilayah. Konsep usulan yang diajukan adalah dengan cara mengembangkan algoritma koordinasi yang memungkinkan suatu pasukan autonomous-drone penjaga keamanan wilayah untuk melakukan penangkapan atau penghentian secara terorganisir terhadap kelompok drone penyerang.Adapun dasar algoritma yang digunakan pada penelitian ini adalah Social Spider Optimization (SSO). Aspek utama kontribusi di dalam penelitian ini terdapat pada modifikasi algoritma SSO untuk mengatasi persoalan pencarian multi-target-bergerak berupa penangkapan atau penghentian serangan drone berkelompok. Penelitian ini telah diuji dalam simulator 3 dimensi, dengan konfigurasi pergerakan kelompok drone penyerang yang diujikan berbasis pendekatan penyerangan sekali jalan. Uji kinerja dilakukan dengan membandingkan kinerja usulan algoritma dengan 3 alternatif algoritma pergerakan kawanan drone pertahanan. Ketika dihadapkan dengan kelompok drone penyerang yang berkerumun, dibandingkan 3 alternatif algoritma pertahanan yang lain, usulan algoritma mampu mengurangi kerusakan hingga rasio 44% (terbaik), 67% (rata-rata), dan 97% (terburuk).

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Nowadays, drone technology development brings many dangerous possibilities for the security of an area. Because of the advanced development, many villains can conduct illegal surveillance or even long-distance attack on an area by remote controlling a drone or programming the drone movement strategy. Meanwhile, the dangerous impact of drone technology usage becomes higher when the technology used to attack an area is the type of drone swarm technology. In this kind of technology, every drone can coordinate and communicate while striking an area.

Although there have been many anti-drone technologies developed at the moment, they tend to focus on targeting one single drone only. So far, no publication comprehensively proposes a framework to overcome the multi-drone attack. This research presents an anti-drone-swarm defense strategy to protect area safety by using the autonomous-drone unit. The proposed method concept is manifested by developing a coordination algorithm for an autonomous-drone army to communicate with each other while catching or stopping the invader drone swarm.

The foundation of this research algorithm is Social Spider Optimization (SSO). This research's main contribution lies in modifying the SSO algorithm to handle the moving multiple-target searching problem, in this case, by catching or stopping the drone-swarm attack. This research has been tested in a 3D simulation environment where invader drone-swarm movement is developed based on a one-way-ticket approach. Performance evaluation is conducted by comparing the proposed method result with 3 other multiple defender drone movement algorithms. When facing an invader drone swarm with crowded formation, compared to the other 3 algorithms, the proposed method produces damage ratios up to 44% (best), 67% (average), and 97% (worst)."

"Nowadays, the pursuance of sustainability obligates manufacturers to redesign products in order to reduce negative environmental impacts. However, only a few studies have simultaneously considered environmental sustainability and assemblability. To bridge this research gap, this study aimed to develop a redesign method based on modular product architecture. This method manages to support a sustainable product considering its materials, assembly sequence and line balance at initial design phase. This method begins with a current product analysis based on economic and environmental performances (i.e., total cost and CO2 emissions). Additionally, new materials and assembly methods are incorporated into redesigning a more sustainable product without compromising production performance. To ensure assemblability, the line balance of 60% is served as one of the constraints. This study applies the particle swarm optimization algorithm to calculate an optimal module organization along with assembly methods and assembly sequences. An air purifier case study is presented to demonstrate the benefits of the proposed method. As a result, the redesigned product can be more easily maintained during product usage and be recycled at the end of product life."

"This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c. programming, and interval analysis.It also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature, from the theoretical as well as from the empirical point of view. Furthermore, extensions of the prototype algorithm for multicriteria global optimization problems as well as mixed combinatorial optimization problems are considered. Numerical examples based on facility location problems support the theory. Applications of geometric branch-and-bound methods, namely the circle detection problem in image processing, the integrated scheduling and location makespan problem, and the median line location problem in the three-dimensional space are also presented.The book is intended for both researchers and students in the areas of mathematics, operations research, engineering, and computer science."

"This book presents the application of some AI related optimization techniques in the operation and control of electric power systems. With practical applications and examples the use of functional analysis, simulated annealing, Tabu-search, Genetic algorithms and fuzzy systems for the optimization of power systems is discussed in detail. Preliminary mathematical concepts are presented before moving to more advanced material."

"This book familiarizes readers with a hierarchical decoupled planning and control strategy that has been proven efficient through research. It is made up of a hierarchy of modules with well defined functions operating at a variety of rates, linked together from top to bottom. The outer loop, closed periodically, consists of a discrete search that produces a set of waypoints leading to the goal while avoiding obstacles and weighed regions. The second level smoothes this set so that the generated paths are feasible given the vehicle's velocity and accelerations limits. The third level generates flyable, timed trajectories and the last one is the tracking controller that attempts to minimize the error between the robot measured trajectory and the reference trajectory. This hierarchy is reflected in the structure and content of the book. Topics treated are : modelling, flight planning, trajectory design and control. Finally, some actual projects are described in the appendix. "

"Quadcopter berukuran kecil dan inersia drone yang rendah memungkinkan penggunaan sistem kontrol penerbangan sederhana, dengan kepraktisan Quadcopter diharapkan dapat dipergunakan untuk penanganan bencana sehingga dapat meningkatkan ketahananwilayah. Quadcopter yang dikeluarkan oleh perusahaan tidak dapat diubah-ubah. penelitian ini dengan merakit quadcopter sendiri dan melakukan perubahan pada pitch propeller untuk mengetahui pengaruh thrust dan thrust motor pada quadcopter tipe x. Dari hasilpenelitian dan perhitungan yang dilakukan, thrust yang dihasilkan dengan sudut puntir (pitch) 50olebih besar dibandingkan dengan sudut puntir (pitch) 43o pada setiap pergerakannya sehingga semakinbesar sudut puntir (pitch), maka semakin besar pula thrust yang dihasilkan oleh propeller tersebut. Voltage yang masuk pada setiap motor akan berbeda tergantung pada pergerakannya. Jadi, semakin besar voltage yang masuk pada motor brushless maka semakin tinggi putaran motornya sehingga thrust yang dihasilkan juga semakin besar."

" PSS (Power system stabilizer) telah digunakan secara luas untuk memperbaiki stabilitas sistem tenaga listrik modern. Dalam makalah ini diusulkan perancangan sistematik PSS dengan Particle Swarm Optimization (PSO) sebagai metode optimasi penalaan parameter PSS. Penalaan parameter PSS dilakukan untuk mendapatkan sistem tenaga listrik yang stabil dan teredam secara optimal. Kriteria optimal yang digunakan dalam proses penalaan parameter adalah indeks performansi Integral of Time multiplied by Absolute Error (ITAE). Performansi dariPSS ini diujikan pada sistem tenaga listrik mesin tunggal dibawah gangguan kecil, kondisi beban dan parameter tertentu. Hasil analisa nilaieigen dan simulasi menunjukkan bahwa osilasi sistem tenaga listrik dapat teredam secara optimal melalui penalaan PSS berbasis PSO ini. Hasil simulasi juga menunjukkan bahwa performansi dinamik PSS berbasis PSO lebih baik dibandingkan PSS yang ditala secara konvensional.

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AbstractPower system stabilizer (PSS) have been extensively used in modern power system for enhancing stability of the system. This paper presents a new systematic approach for the design of power systemstabilizer using PSO (Particle Swarm Optimization). The proposed approach employs PSO search for optimal setting of PSS parameters. The optimal criteria of the Integral of Time multiplied by AbsoluteError (ITAE) is used to search optimal setting. The performance of the proposed PSS under small disturbances, loading conditions and system parameters is tested. The eigenvalue analysis and simulationresults show the effectiveness of the PSO based PSS to damp out the system oscillations. It is found that the dynamic performance with the PSO based PSS shows improved results, over conventionally tunedPSS."

"ABSTRAKPenelitian ini bertujuan untuk melakukan simulasi interferensi dari 2 drone keFixed Service (FS) dengan memperhitungkan faktor redaman hujan, dimana UASyang di teliti adalah sistem pesawat tanpa awak dengan kendali satelit dan FSadalah sistem komunikasi terrestrial di permukaan bumi. Evaluasi kompatibilitasdisini adalah pada penggunaan frekuensi yang sama pada kedua sistem tersebutyang dimodelkan dengan interferensi antara dua sistem. Dan berdasarkan WRC-15 (World Radiocommunication, 2015), bahwa salah satu spektrum alokasifrekuensi untuk Fixed Satellite Service (FSS) di wilayah 3 (Asia dan Oceania)adalah di frekuensi 12,5-12,75 GHz dimana pita frekuensi ini berbagi denganfrekuensi FS. Skenario interferensi antara kedua sistem ini diamati pada saat 2-drone terbang di atas wilayah FS, dan simulasi dilakukan untuk menyelidikigangguan dari emisi 2 drone ke penerima FS. Dimana hasil simulasi menunjukkanbahwa interferensi dari 2 drone tidak melewati batas yang telah ditentukan,sehingga masih aman bagi drone dan FS.

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ABSTRACTThis study aims to evaluate the compatibility of spectrum Unmanned AircraftSystem (UAS) with the number of 2 drone and Fixed Service (FS) with rainattenuation, which the UAS in the study is the system of unmanned aircraft withsatellite control and FS is a terrestrial communication system on the earthsurface. Evaluation of compatibility here is the use of the same frequency in bothsystems, are modeled by interference between the two systems. And by WRC-15(World Radiocommunication, 2015), that one of the frequency spectrum allocatedfor the Fixed Satellite Service (FSS) in region 3 (Asia and Oceania) is in the 12.5to 12.75 GHz frequency band, which is shared with the frequency FS. Interferencescenario between the two systems is observed during several drones flying overthe area FS, and simulations were carried out to investigate the disturbance of theemissions of 2 drone to the FS receiver. Wherein the simulation results show thatthe interference of drones did not cross the line that has been determined, so it isstill safe for the drones and FS"

"Isi tesis ini mengenai pembuatan simulator 3D dari algoritma pencarian Particle Swarm Optimization untuk pencarian banyak sumber asap dengan menggunakan Open Dynamics Engine dan mengenai Dynamic Niche-PSO yang adalah algoritma baru sebagai modifikasi algoritma MPSO dari penelitian sebelumnya [1]. Versi simulator 2D untuk PSO ini telah dibuat penelitian sebelumnya ini. Pemodelan fisik 3D ini bertujuan untuk mengurangi gap antara perangkat lunak dan perangkat keras di dunia nyata.Salah satu bab adalah bab yang menjelaskan pembuatan model robot, asap dan sumbernya, dan medan dengan Open Dynamics Engine. Dilanjutkan dengan bab tentang cara pemakaian GUI simulator ini.Algoritma Dynamic Niche-PSO yang diajukan pada penelitian ini bertujuan untuk memperbaiki kelemahan algoritma PSO sebelumnya dimana 2 niche (kelompok agen) atau lebih masih ada kemungkinan untuk menuju sumber asap yang sama. Pada Dynamic Niche-PSO ini diperkenalkan robot baru, yaitu robot utama yang mempunyai arca ketertarikan. Pada Dynamic Niche-PSO ini juga robot netral dan bermuatan dapat berpindah keanggotaan dari satu niche ke niche yang lain apabila memasuki arca ketertarikan atau attract area dari robot utama niche yang lain ini.

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The contents of this thesis are the development of 3D simulator for visualizing Particle Swarm Optimization algorithm for multi odor source localization using Open Dynamics Engine, and Dynamic Niche-PSO as modification of MPSO algorithm from previous research [1]. The 2D version of this MPSO is made in previous research. This 3D modeling has a purpose to reducegap between Software and Hardware in the real world.

One of chapters is explaining about how to make the model of robots, plumes and its sources, and field with Open Dynamics Engine. Continued with chapter explaining about how to use the GUI of this simulator.

Dynamic Niche-PSO algorithm proposed in this research has a purpose to refine the weakness of previous algorithm where 2 niches (group of agents) or more still have a probability to move toward the same odor source. There is newly introduced robot in this Dynamic Niche-PSO algorithm called main robot which has an attract area. In this Dynamic Niche-PSO also a neutral robot or a charge robot could become a member of another niche if it entered the attract area of main robot of this other niche."